In [1]:
import pandas as pd
import re
In [2]:
df = pd.read_csv('data_tau_ta.csv')
In [3]:
df.head()
Out[3]:
title
date
days
tokens
stem
lemma
pos_tags
named_entities
0
Deep Advances in Generative Modeling
6 points by gwulfs 5 hours ago | discuss
1
deep,advances,generative,modeling
Deep Advances in Generative Model
Deep Advances in Generative Modeling
[('Deep', 'JJ'), ('Advances', 'NNS'), ('in', '...
['Generative Modeling']
1
A Neural Network in 11 lines of Python
2 points by dekhtiar 5 hours ago | discuss
1
neural,network,11,lines,python
A Neural Network in 11 lines of Python
A Neural Network in 11 lines of Python
[('A', 'DT'), ('Neural', 'NNP'), ('Network', '...
['Python']
2
Python, Machine Learning, and Language Wars
3 points by pmigdal 7 hours ago | discuss
1
python,machine,learning,language,wars
Python, Machine Learning, and Language War
Python, Machine Learning, and Language Wars
[('Python', 'NNP'), (',', ','), ('Machine', 'N...
['Python', 'Machine Learning', 'Language Wars']
3
Markov Chains Explained Visually
11 points by zeroviscosity 1 day ago | 1 comment
1
markov,chains,explained,visually
Markov Chains Explained Visu
Markov Chains Explained Visually
[('Markov', 'NNP'), ('Chains', 'NNP'), ('Expla...
['Markov Chains', 'Visually']
4
Dplython: Dplyr for Python
10 points by thenaturalist 1 day ago | 3 comm...
1
dplython,dplyr,python
Dplython: Dplyr for Python
Dplython: Dplyr for Python
[('Dplython', 'NN'), (':', ':'), ('Dplyr', 'NN...
['Dplython', 'Python']
In [4]:
df.shape
Out[4]:
(180, 8)
In [5]:
import nltk
In [6]:
from nltk.corpus import stopwords
In [7]:
stop = stopwords.words('english')
In [8]:
stop.extend(('.', ',', '"', "'", '?', '!', ':', ';', '(', ')', '[', ']', '{', '}','/','-'))
In [9]:
tokens_list = df['tokens'].tolist()
In [10]:
tokens_list
Out[10]:
['deep,advances,generative,modeling',
'neural,network,11,lines,python',
'python,machine,learning,language,wars',
'markov,chains,explained,visually',
'dplython,dplyr,python',
'inferring,causal,impact,using,bayesian,structural,time,series,models',
'tutorial,web,scraping,mapping,breweries,import,io,r',
'billion,taxi,rides,amazon,emr,running,spark',
'rise,greedy,robots',
'extracting,image,metadata,scale',
'python,data,structures,algorithms,interviews',
'lift,charts,data,scientist,secret,weapon',
'become,machine,learning,expert,one,simple,step',
'data,science,side,project',
'simple,estimation,hierarchical,events,petersburg',
'engineers,write,etl,high,functioning,data,science,departments',
'unsupervised,computer,vision,current,state,art',
'data,visualization,tools,r,dataisbeautiful,oc,creators,use',
'data,engineering,slack,twelve,mistakes,made,first,three,months',
'unusual,interactive,machine,learning,challenge',
'datumbox,machine,learning,framework,0,7,0,released',
'reshaping,pandas',
'data,science,intro,math,phys,background',
'neural,networks,demystified',
'machines,learn,apple,watch,detecting,undiagnosed,heart,condition',
'data,science,tools,biggest,winners,losers',
'10,years,open,source,machine,learning',
'jobs,run,families',
'conversion,rate,changed,bayesian,timeseries,analysis,python',
'xgboost4j,portable,distributed,xgboost,spark,flink,dataflow',
'introduction,scikit,flow,simplified,interface,tensorflow',
'learn,machine,learning',
'deep,roots,javascript,fatigue',
'make,data,tau,work',
'machine,learning,depth,non,technical,guide,???,part,4',
'data,science,slack,channel,click,invite',
'genomic,data,visualization,using,python',
'descriptive,statistics,sql',
'playing,moneyball,ea,fifa,16',
'intellexer,natural,language,processing,text,mining,rest,api',
'use,cohort,data,analyze,user,behavior',
'show,dt,datasets,co,easy,way,share,discover,ml,datasets',
'ode,rice,cooker,smartest,kitchen,appliance,ever,owned',
'making,transparent,variations,analytical,choices,affect,results',
'ask,dt,rookie,mistakes,r',
'scala,better,choice,python,apache,spark',
'julia,fast,language,numerical,computing',
'analyzing,golden,state,warriors,passing,network,using,graphframes,spark',
'megaman,manifold,learning,millions,points',
'detect,outliers,parametric,non,parametric,methods',
'ballr,interactive,nba,shot,charts,r,shiny',
'minecraft,run,artificial,intelligence,experiments',
'deep,q,learning,space,invaders',
'theano,tutorial',
'computing,classification,evaluation,metrics,r',
'personality,space,cartoon,characters',
'announcing,apache,flink,1,0,0',
'bayesian,reasoning,twilight,zone',
'bayesian,estimation,g,train,wait,times',
'experiments,explaining,complex,black,box,ensemble,predictions',
'creating,hadoop,pseudo,distributed,environment',
'data,science,pop,austin,tx',
'billion,taxi,rides,amazon,emr,running,presto',
'train,image,classifier,inception,tensorflow',
'statisticians,agree,time,stop,misusing,p,value',
'shiny,app,running,tensorflow,demo',
'file,details,owners,gitnoc,git,pandas',
'7,big,data,technologies,use,data,engineers,know',
'topic,clusters,tf,idf,vectorization,spark,scala',
'neural,doodles,workflows,next,generation,artists',
'graph,databases,101',
'telemetry,collectd,logstash,elasticsearch,grafana,elg',
'xgboost,scalable,tree,boosting,system,article',
'dataradar,io,data,science,rss,feed,enough,data,data',
'international,women,day,#,pledgeforparity,means,us',
'top,50,data,science,thought,leaders,twitter',
'ask,dt,hiring,march,2016',
'introducing,graphframes',
'announcing,r,tools,visual,studio',
'question,want,say,working,data',
'genomic,ranges,introduction,working,genomic,data',
'tensorflow,poets',
'unsupervised,learning,even,less,supervision,using,bayesian,optimization',
'work,large,json,datasets,using,python,pandas',
'drivendata,competition,model,visualize,fog,patterns,morocco',
'deriving,better,insights,time,series,data,cycle,plots',
'deep,learning,nine,lectures,coll,ge,de,france,yan,lecun',
'sql,data,analysis',
'stream,processing,messaging,systems,iot,age',
'optimizing,facebook,campaigns,r',
'trump,tweets,globe,aka,fun,d3,socket,io,twitter,api',
'pandas,users,excited,apache,arrow',
'histogram,intersection,change,detection',
'simpler,way,merge,data,streams',
'distributed,tensorflow,open,sourced',
'd3,js,screencasts,1,3,free',
'regression,classification,examples,r',
'free,online,course,statistical,shape,modelling',
'worry,deep,learning,deepen,understanding,causality,instead',
'skizze,high,throughput,probabilistic,data,structure,service,storage',
'work,private,repositories,updates,flyelephant,platform',
'import,xml,almost,anywhere',
'optimizing,notification,timing,one,signal',
'survival,analysis,cricket,player,careers',
'generate,image,analogies,using,neural,matching,blending',
'analyzing,1,8m,tweets,super,bowl,50,twython,twitter,api,aylien',
'newly,released,sklearn,compatible,library,categorical,encoders',
'watch,tiny,neural,nets,learn',
'four,pitfalls,hill,climbing,animated,look',
'decision,forests,convolutional,networks,models',
'math,genius,hacked,okcupid,find,true,love',
'developers,pylearn2',
'density,estimation,dirichlet,process,mixtures,using,pymc3',
'using,survival,analysis,git,pandas,estimate,code,quality',
'analysis,flint,michigan,water,crisis,part,1,initial,corrosivity',
'analysis,republican,twitter,follower,interests',
'introduction,ml,talk',
'glove,vs,word2vec,revisited',
'undergrad,data,analysis,science,internships,sf,bay',
'role,statistical,significance,growth,hacking',
'data,science,course,@,harvard',
'principal,component,projection,without,principal,component,analysis',
'machine,learning,depth,non,technical,guide,part,3',
'stochastic,dummy,boosting',
'interactive,map,hong,kong,lense,instagram',
'data,science,monsanto',
'data,science,instacart',
'building,streaming,search,platform',
'sneak,peak,cloud,2,minute,intro,beginners',
'win,vector,video,courses,price,status,changes',
'50,+,data,science,machine,learning,cheat,sheets',
'one,reason,scared,deep,learning',
'visual,logic,authoring,vs,code',
'data,science,python,online,training,hands,experience',
'viewing,us,presidential,primary,lens,twitter',
'caffe,spark,open,sourced',
'ethical,data,scientist',
'answers,frequently,asked,questions,machine,learning',
'intro,b,testing,p,values',
'visualizing,state,level,data,r,statebins',
'probabilistic,graphical,models,slides,&,video,lectures,eric,xing,cmu',
'sense2vec,spacy,gensim',
'code,understand,deepmind,neural,stack,machine,python',
'make,polished,jupyter,presentations,optional,code,visibility',
'become,bayesian,eight,easy,steps',
'optimizing,.*:,details,vectorization,metaprogramming,julia',
'ibm,certified,apache,spark,online,training',
'geographic,data,science,course',
'daily,mail,stole,visualization,twice',
'ensemble,methods,improved,machine,learning,results',
'apache,spark,unsupervised,learning,security',
'machinejs,automated,machine,learning,give,data,file',
'kafka,producer,latency,large,topic,counts',
'nsa,skynet,program,may,killing,thousands,innocent,people',
'overoptimizing,story,kaggle',
'big,dimensions',
'automate,oscars,pool,r',
'signal,processing,ligo,gw150914,data',
'overview,dezyre,coursera,data,science,course',
'upcoming,datathon,nyc',
'summarizing,data,sql',
'b,testing,scammers',
'highly,interpretable,classifiers,scikit,learn,using,bayesian,decision,rules',
'auto,scaling,scikit,learn,spark',
'f,***,park',
'machine,learning,depth,non,technical,guide,part,2',
'webhose,io,offers,historical,data,archive',
'meetup,introduction,machine,learning,algorithms,data,science',
'exploring,limits,language,modeling',
'text,mining,south,park',
'finding,k,k,means,parametric,bootstrap',
'billion,nyc,taxi,uber,rides,aws,redshift',
'getting,started,statistics,data,science',
'rodeo,1,3,tab,completion,docstrings',
'teaching,d3,js,links',
'parallel,scikit,learn,yarn',
'meetup,free,live,webinar,prescriptive,analytics,fun,profit',
'access,vk,com,vkontakte,api,via,r',
'deep,learning,tutorial,lecun,bengio',
'machine,learning,meets,economics']
In [11]:
# Let us get the frequency count
frequency_words = {}
for data in tokens_list:
data = data.replace("[","")
data = data.replace("]","")
data = data.replace("'","")
data_list = data.split(',')
print(data_list)
for token in data_list:
token = token.rstrip()
token = token.lstrip()
if token not in stop:
if token in frequency_words:
count = frequency_words[token]
count = count + 1
frequency_words[token] = count
else:
frequency_words[token] = 1
['deep', 'advances', 'generative', 'modeling']
['neural', 'network', '11', 'lines', 'python']
['python', 'machine', 'learning', 'language', 'wars']
['markov', 'chains', 'explained', 'visually']
['dplython', 'dplyr', 'python']
['inferring', 'causal', 'impact', 'using', 'bayesian', 'structural', 'time', 'series', 'models']
['tutorial', 'web', 'scraping', 'mapping', 'breweries', 'import', 'io', 'r']
['billion', 'taxi', 'rides', 'amazon', 'emr', 'running', 'spark']
['rise', 'greedy', 'robots']
['extracting', 'image', 'metadata', 'scale']
['python', 'data', 'structures', 'algorithms', 'interviews']
['lift', 'charts', 'data', 'scientist', 'secret', 'weapon']
['become', 'machine', 'learning', 'expert', 'one', 'simple', 'step']
['data', 'science', 'side', 'project']
['simple', 'estimation', 'hierarchical', 'events', 'petersburg']
['engineers', 'write', 'etl', 'high', 'functioning', 'data', 'science', 'departments']
['unsupervised', 'computer', 'vision', 'current', 'state', 'art']
['data', 'visualization', 'tools', 'r', 'dataisbeautiful', 'oc', 'creators', 'use']
['data', 'engineering', 'slack', 'twelve', 'mistakes', 'made', 'first', 'three', 'months']
['unusual', 'interactive', 'machine', 'learning', 'challenge']
['datumbox', 'machine', 'learning', 'framework', '0', '7', '0', 'released']
['reshaping', 'pandas']
['data', 'science', 'intro', 'math', 'phys', 'background']
['neural', 'networks', 'demystified']
['machines', 'learn', 'apple', 'watch', 'detecting', 'undiagnosed', 'heart', 'condition']
['data', 'science', 'tools', 'biggest', 'winners', 'losers']
['10', 'years', 'open', 'source', 'machine', 'learning']
['jobs', 'run', 'families']
['conversion', 'rate', 'changed', 'bayesian', 'timeseries', 'analysis', 'python']
['xgboost4j', 'portable', 'distributed', 'xgboost', 'spark', 'flink', 'dataflow']
['introduction', 'scikit', 'flow', 'simplified', 'interface', 'tensorflow']
['learn', 'machine', 'learning']
['deep', 'roots', 'javascript', 'fatigue']
['make', 'data', 'tau', 'work']
['machine', 'learning', 'depth', 'non', 'technical', 'guide', '???', 'part', '4']
['data', 'science', 'slack', 'channel', 'click', 'invite']
['genomic', 'data', 'visualization', 'using', 'python']
['descriptive', 'statistics', 'sql']
['playing', 'moneyball', 'ea', 'fifa', '16']
['intellexer', 'natural', 'language', 'processing', 'text', 'mining', 'rest', 'api']
['use', 'cohort', 'data', 'analyze', 'user', 'behavior']
['show', 'dt', 'datasets', 'co', 'easy', 'way', 'share', 'discover', 'ml', 'datasets']
['ode', 'rice', 'cooker', 'smartest', 'kitchen', 'appliance', 'ever', 'owned']
['making', 'transparent', 'variations', 'analytical', 'choices', 'affect', 'results']
['ask', 'dt', 'rookie', 'mistakes', 'r']
['scala', 'better', 'choice', 'python', 'apache', 'spark']
['julia', 'fast', 'language', 'numerical', 'computing']
['analyzing', 'golden', 'state', 'warriors', 'passing', 'network', 'using', 'graphframes', 'spark']
['megaman', 'manifold', 'learning', 'millions', 'points']
['detect', 'outliers', 'parametric', 'non', 'parametric', 'methods']
['ballr', 'interactive', 'nba', 'shot', 'charts', 'r', 'shiny']
['minecraft', 'run', 'artificial', 'intelligence', 'experiments']
['deep', 'q', 'learning', 'space', 'invaders']
['theano', 'tutorial']
['computing', 'classification', 'evaluation', 'metrics', 'r']
['personality', 'space', 'cartoon', 'characters']
['announcing', 'apache', 'flink', '1', '0', '0']
['bayesian', 'reasoning', 'twilight', 'zone']
['bayesian', 'estimation', 'g', 'train', 'wait', 'times']
['experiments', 'explaining', 'complex', 'black', 'box', 'ensemble', 'predictions']
['creating', 'hadoop', 'pseudo', 'distributed', 'environment']
['data', 'science', 'pop', 'austin', 'tx']
['billion', 'taxi', 'rides', 'amazon', 'emr', 'running', 'presto']
['train', 'image', 'classifier', 'inception', 'tensorflow']
['statisticians', 'agree', 'time', 'stop', 'misusing', 'p', 'value']
['shiny', 'app', 'running', 'tensorflow', 'demo']
['file', 'details', 'owners', 'gitnoc', 'git', 'pandas']
['7', 'big', 'data', 'technologies', 'use', 'data', 'engineers', 'know']
['topic', 'clusters', 'tf', 'idf', 'vectorization', 'spark', 'scala']
['neural', 'doodles', 'workflows', 'next', 'generation', 'artists']
['graph', 'databases', '101']
['telemetry', 'collectd', 'logstash', 'elasticsearch', 'grafana', 'elg']
['xgboost', 'scalable', 'tree', 'boosting', 'system', 'article']
['dataradar', 'io', 'data', 'science', 'rss', 'feed', 'enough', 'data', 'data']
['international', 'women', 'day', '#', 'pledgeforparity', 'means', 'us']
['top', '50', 'data', 'science', 'thought', 'leaders', 'twitter']
['ask', 'dt', 'hiring', 'march', '2016']
['introducing', 'graphframes']
['announcing', 'r', 'tools', 'visual', 'studio']
['question', 'want', 'say', 'working', 'data']
['genomic', 'ranges', 'introduction', 'working', 'genomic', 'data']
['tensorflow', 'poets']
['unsupervised', 'learning', 'even', 'less', 'supervision', 'using', 'bayesian', 'optimization']
['work', 'large', 'json', 'datasets', 'using', 'python', 'pandas']
['drivendata', 'competition', 'model', 'visualize', 'fog', 'patterns', 'morocco']
['deriving', 'better', 'insights', 'time', 'series', 'data', 'cycle', 'plots']
['deep', 'learning', 'nine', 'lectures', 'coll', 'ge', 'de', 'france', 'yan', 'lecun']
['sql', 'data', 'analysis']
['stream', 'processing', 'messaging', 'systems', 'iot', 'age']
['optimizing', 'facebook', 'campaigns', 'r']
['trump', 'tweets', 'globe', 'aka', 'fun', 'd3', 'socket', 'io', 'twitter', 'api']
['pandas', 'users', 'excited', 'apache', 'arrow']
['histogram', 'intersection', 'change', 'detection']
['simpler', 'way', 'merge', 'data', 'streams']
['distributed', 'tensorflow', 'open', 'sourced']
['d3', 'js', 'screencasts', '1', '3', 'free']
['regression', 'classification', 'examples', 'r']
['free', 'online', 'course', 'statistical', 'shape', 'modelling']
['worry', 'deep', 'learning', 'deepen', 'understanding', 'causality', 'instead']
['skizze', 'high', 'throughput', 'probabilistic', 'data', 'structure', 'service', 'storage']
['work', 'private', 'repositories', 'updates', 'flyelephant', 'platform']
['import', 'xml', 'almost', 'anywhere']
['optimizing', 'notification', 'timing', 'one', 'signal']
['survival', 'analysis', 'cricket', 'player', 'careers']
['generate', 'image', 'analogies', 'using', 'neural', 'matching', 'blending']
['analyzing', '1', '8m', 'tweets', 'super', 'bowl', '50', 'twython', 'twitter', 'api', 'aylien']
['newly', 'released', 'sklearn', 'compatible', 'library', 'categorical', 'encoders']
['watch', 'tiny', 'neural', 'nets', 'learn']
['four', 'pitfalls', 'hill', 'climbing', 'animated', 'look']
['decision', 'forests', 'convolutional', 'networks', 'models']
['math', 'genius', 'hacked', 'okcupid', 'find', 'true', 'love']
['developers', 'pylearn2']
['density', 'estimation', 'dirichlet', 'process', 'mixtures', 'using', 'pymc3']
['using', 'survival', 'analysis', 'git', 'pandas', 'estimate', 'code', 'quality']
['analysis', 'flint', 'michigan', 'water', 'crisis', 'part', '1', 'initial', 'corrosivity']
['analysis', 'republican', 'twitter', 'follower', 'interests']
['introduction', 'ml', 'talk']
['glove', 'vs', 'word2vec', 'revisited']
['undergrad', 'data', 'analysis', 'science', 'internships', 'sf', 'bay']
['role', 'statistical', 'significance', 'growth', 'hacking']
['data', 'science', 'course', '@', 'harvard']
['principal', 'component', 'projection', 'without', 'principal', 'component', 'analysis']
['machine', 'learning', 'depth', 'non', 'technical', 'guide', 'part', '3']
['stochastic', 'dummy', 'boosting']
['interactive', 'map', 'hong', 'kong', 'lense', 'instagram']
['data', 'science', 'monsanto']
['data', 'science', 'instacart']
['building', 'streaming', 'search', 'platform']
['sneak', 'peak', 'cloud', '2', 'minute', 'intro', 'beginners']
['win', 'vector', 'video', 'courses', 'price', 'status', 'changes']
['50', '+', 'data', 'science', 'machine', 'learning', 'cheat', 'sheets']
['one', 'reason', 'scared', 'deep', 'learning']
['visual', 'logic', 'authoring', 'vs', 'code']
['data', 'science', 'python', 'online', 'training', 'hands', 'experience']
['viewing', 'us', 'presidential', 'primary', 'lens', 'twitter']
['caffe', 'spark', 'open', 'sourced']
['ethical', 'data', 'scientist']
['answers', 'frequently', 'asked', 'questions', 'machine', 'learning']
['intro', 'b', 'testing', 'p', 'values']
['visualizing', 'state', 'level', 'data', 'r', 'statebins']
['probabilistic', 'graphical', 'models', 'slides', '&', 'video', 'lectures', 'eric', 'xing', 'cmu']
['sense2vec', 'spacy', 'gensim']
['code', 'understand', 'deepmind', 'neural', 'stack', 'machine', 'python']
['make', 'polished', 'jupyter', 'presentations', 'optional', 'code', 'visibility']
['become', 'bayesian', 'eight', 'easy', 'steps']
['optimizing', '.*:', 'details', 'vectorization', 'metaprogramming', 'julia']
['ibm', 'certified', 'apache', 'spark', 'online', 'training']
['geographic', 'data', 'science', 'course']
['daily', 'mail', 'stole', 'visualization', 'twice']
['ensemble', 'methods', 'improved', 'machine', 'learning', 'results']
['apache', 'spark', 'unsupervised', 'learning', 'security']
['machinejs', 'automated', 'machine', 'learning', 'give', 'data', 'file']
['kafka', 'producer', 'latency', 'large', 'topic', 'counts']
['nsa', 'skynet', 'program', 'may', 'killing', 'thousands', 'innocent', 'people']
['overoptimizing', 'story', 'kaggle']
['big', 'dimensions']
['automate', 'oscars', 'pool', 'r']
['signal', 'processing', 'ligo', 'gw150914', 'data']
['overview', 'dezyre', 'coursera', 'data', 'science', 'course']
['upcoming', 'datathon', 'nyc']
['summarizing', 'data', 'sql']
['b', 'testing', 'scammers']
['highly', 'interpretable', 'classifiers', 'scikit', 'learn', 'using', 'bayesian', 'decision', 'rules']
['auto', 'scaling', 'scikit', 'learn', 'spark']
['f', '***', 'park']
['machine', 'learning', 'depth', 'non', 'technical', 'guide', 'part', '2']
['webhose', 'io', 'offers', 'historical', 'data', 'archive']
['meetup', 'introduction', 'machine', 'learning', 'algorithms', 'data', 'science']
['exploring', 'limits', 'language', 'modeling']
['text', 'mining', 'south', 'park']
['finding', 'k', 'k', 'means', 'parametric', 'bootstrap']
['billion', 'nyc', 'taxi', 'uber', 'rides', 'aws', 'redshift']
['getting', 'started', 'statistics', 'data', 'science']
['rodeo', '1', '3', 'tab', 'completion', 'docstrings']
['teaching', 'd3', 'js', 'links']
['parallel', 'scikit', 'learn', 'yarn']
['meetup', 'free', 'live', 'webinar', 'prescriptive', 'analytics', 'fun', 'profit']
['access', 'vk', 'com', 'vkontakte', 'api', 'via', 'r']
['deep', 'learning', 'tutorial', 'lecun', 'bengio']
['machine', 'learning', 'meets', 'economics']
In [12]:
frequency_words['data']
Out[12]:
41
tf–idf, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.[1]:8 It is often used as a weighting factor in information retrieval and text mining. The tf-idf value increases proportionally to the number of times a word appears in the document, but is offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words appear more frequently in general.
Variations of the tf–idf weighting scheme are often used by search engines as a central tool in scoring and ranking a document's relevance given a user query. tf–idf can be successfully used for stop-words filtering in various subject fields including text summarization and classification.
Let us start with the "Term Frequency" - TF
In [13]:
df_tfidf = pd.DataFrame(data=list(frequency_words.items()),columns=['word','tf'])
In [14]:
df_tfidf.head()
Out[14]:
word
tf
0
matching
1
1
detection
1
2
yan
1
3
supervision
1
4
cooker
1
In [15]:
df_tfidf.sort_values(ascending=False, by = "tf", inplace=True)
In [16]:
df_tfidf.head()
Out[16]:
word
tf
415
data
41
148
learning
23
231
science
18
462
machine
16
230
r
11
Let us get in how many documents (each title) does the word occur
In [17]:
def get_documents_count(row):
document_counter = 0
word = row['word']
for document in df.tokens:
document = document.replace("'",'')
document = document.replace("[",'')
document = document.replace("]",'')
document = document.split(',')
document = map(str.strip,document)
if word in document:
document_counter = document_counter + 1
return document_counter
In [18]:
df_tfidf['document_count'] = df_tfidf.apply(get_documents_count,axis=1)
In [19]:
df_tfidf.head()
Out[19]:
word
tf
document_count
415
data
41
38
148
learning
23
23
231
science
18
18
462
machine
16
16
230
r
11
11
In [20]:
df_tfidf.tail()
Out[20]:
word
tf
document_count
249
facebook
1
1
250
tf
1
1
251
michigan
1
1
252
roots
1
1
658
structures
1
1
In [21]:
# we already have the count of all the documents
total_docs = df.shape[0]
In [22]:
total_docs
Out[22]:
180
In [23]:
import math
from wordcloud import WordCloud
import matplotlib.pyplot as plt
%matplotlib inline
In [24]:
def compute_tfidf(row):
idf = math.log10(total_docs/row['document_count'])
return row['tf'] * idf
In [25]:
df_tfidf['tfidf'] = df_tfidf.apply(compute_tfidf,axis=1)
In [26]:
df_tfidf.head()
Out[26]:
word
tf
document_count
tfidf
415
data
41
38
27.695045
148
learning
23
23
20.551527
231
science
18
18
18.000000
462
machine
16
16
16.818440
230
r
11
11
13.352678
In [27]:
df_tfidf.tail()
Out[27]:
word
tf
document_count
tfidf
249
facebook
1
1
2.255273
250
tf
1
1
2.255273
251
michigan
1
1
2.255273
252
roots
1
1
2.255273
658
structures
1
1
2.255273
In [28]:
df_tfidf.sort_values(by='tfidf',ascending=True,inplace=True)
In [29]:
df_tfidf.head()
Out[29]:
word
tf
document_count
tfidf
491
condition
1
1
2.255273
155
twice
1
1
2.255273
145
growth
1
1
2.255273
146
choices
1
1
2.255273
149
app
1
1
2.255273
In [30]:
df_tfidf.replace(to_replace=0.0,value=0.1,inplace=True)
In [31]:
df_tfidf.tail()
Out[31]:
word
tf
document_count
tfidf
230
r
11
11
13.352678
462
machine
16
16
16.818440
231
science
18
18
18.000000
148
learning
23
23
20.551527
415
data
41
38
27.695045
In [32]:
df_tfidf.set_index('word', inplace=True)
In [33]:
df_tfidf.head()
Out[33]:
tf
document_count
tfidf
word
condition
1
1
2.255273
twice
1
1
2.255273
growth
1
1
2.255273
choices
1
1
2.255273
app
1
1
2.255273
now let us plot a word cloud to see the prominence of the word
In [34]:
wordcloud = WordCloud()
In [35]:
word_tfidf = df_tfidf['tfidf'].to_dict()
In [36]:
wordcloud.generate_from_frequencies(word_tfidf.items())
plt.figure(figsize=(14,10))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
In natural language processing, Latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics.
Original Paper on LDA - http://jmlr.org/papers/v3/blei03a.html
Summary - We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.
Here is a graphical approach to build intuition around this topic - http://www.mblondel.org/journal/2010/08/21/latent-dirichlet-allocation-in-python/
Here is a video which explains LDA - https://www.youtube.com/watch?v=ePUAZ8RG-3w
In [37]:
import lda
import numpy as np
import lda.datasets
import sklearn.feature_extraction.text as text
In [38]:
vectorizer = text.CountVectorizer(input='content', stop_words='english', min_df=1)
In [39]:
dtm = vectorizer.fit_transform(df.title).toarray()
In [40]:
dtm
Out[40]:
array([[0, 0, 0, ..., 0, 0, 0],
[0, 0, 1, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
...,
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0]], dtype=int64)
In [41]:
vocab = np.array(vectorizer.get_feature_names())
In [42]:
vocab[:20]
Out[42]:
array(['10', '101', '11', '16', '2016', '50', '8m', 'access', 'advances',
'affect', 'age', 'agree', 'aka', 'algorithms', 'amazon',
'analogies', 'analysis', 'analytical', 'analytics', 'analyze'],
dtype='<U15')
In [43]:
titles = df.title
In [44]:
model = lda.LDA(n_topics=5, n_iter=500, random_state=1)
In [45]:
model.fit(dtm)
Out[45]:
<lda.lda.LDA at 0x118812a20>
In [46]:
model.topic_word_
Out[46]:
array([[ 5.36834272e-03, 5.31519082e-05, 5.31519082e-05, ...,
5.31519082e-05, 5.31519082e-05, 5.31519082e-05],
[ 5.31519082e-05, 5.31519082e-05, 5.31519082e-05, ...,
5.31519082e-05, 5.36834272e-03, 5.31519082e-05],
[ 4.75873227e-05, 4.80631960e-03, 4.75873227e-05, ...,
4.75873227e-05, 4.75873227e-05, 4.75873227e-05],
[ 4.42203944e-05, 4.42203944e-05, 4.46625984e-03, ...,
4.46625984e-03, 4.42203944e-05, 4.46625984e-03],
[ 6.09236018e-05, 6.09236018e-05, 6.09236018e-05, ...,
6.09236018e-05, 6.09236018e-05, 6.09236018e-05]])
In [47]:
topic_word = model.topic_word_
In [48]:
topic_word
Out[48]:
array([[ 5.36834272e-03, 5.31519082e-05, 5.31519082e-05, ...,
5.31519082e-05, 5.31519082e-05, 5.31519082e-05],
[ 5.31519082e-05, 5.31519082e-05, 5.31519082e-05, ...,
5.31519082e-05, 5.36834272e-03, 5.31519082e-05],
[ 4.75873227e-05, 4.80631960e-03, 4.75873227e-05, ...,
4.75873227e-05, 4.75873227e-05, 4.75873227e-05],
[ 4.42203944e-05, 4.42203944e-05, 4.46625984e-03, ...,
4.46625984e-03, 4.42203944e-05, 4.46625984e-03],
[ 6.09236018e-05, 6.09236018e-05, 6.09236018e-05, ...,
6.09236018e-05, 6.09236018e-05, 6.09236018e-05]])
In [49]:
n_top_words = 8
In [50]:
for i, topic_dist in enumerate(topic_word):
topic_words = np.array(vocab)[np.argsort(topic_dist)][:-n_top_words:-1]
print('Topic {}: {}'.format(i, ' '.join(topic_words)))
Topic 0: spark apache pandas code work taxi open
Topic 1: learning machine deep non guide interactive depth
Topic 2: data science course introduction sql use genomic
Topic 3: python using analysis bayesian neural learn scikit
Topic 4: twitter io language api processing d3 free
In [51]:
doc_topic = model.doc_topic_
In [52]:
for n in range(10):
topic_most_pr = doc_topic[n].argmax()
print("topic: {} , {}".format(topic_most_pr,titles[n]))
topic: 4 , Deep Advances in Generative Modeling
topic: 3 , A Neural Network in 11 lines of Python
topic: 1 , Python, Machine Learning, and Language Wars
topic: 2 , Markov Chains Explained Visually
topic: 3 , Dplython: Dplyr for Python
topic: 3 , Inferring causal impact using Bayesian structural time-series models
topic: 4 , Tutorial: Web scraping and mapping breweries with import.io and R
topic: 0 , A Billion Taxi Rides on Amazon EMR running Spark
topic: 3 , The rise of greedy robots
topic: 3 , Extracting image metadata at scale
Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation (see appraisal theory), affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader).
A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level — whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. We will use knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored.
Here is a link to the Sentiment Analysis from nltk site - http://www.nltk.org/howto/sentiment.html
Here is an example of Sentiment Analysis on Tweets data - http://www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/
In [53]:
from nltk.classify import NaiveBayesClassifier
import math
import collections
In [54]:
pos_features = []
neg_features = []
In [55]:
def make_full_dict(word):
return dict([(word, True)])
In [56]:
with open('postive_words.txt','r') as posFile:
lines = posFile.readlines()
for line in lines:
pos_features.append([make_full_dict(line.rstrip()),'pos'])
In [57]:
pos_features
Out[57]:
[[{'a+': True}, 'pos'],
[{'abound': True}, 'pos'],
[{'abounds': True}, 'pos'],
[{'abundance': True}, 'pos'],
[{'abundant': True}, 'pos'],
[{'accessable': True}, 'pos'],
[{'accessible': True}, 'pos'],
[{'acclaim': True}, 'pos'],
[{'acclaimed': True}, 'pos'],
[{'acclamation': True}, 'pos'],
[{'accolade': True}, 'pos'],
[{'accolades': True}, 'pos'],
[{'accommodative': True}, 'pos'],
[{'accomodative': True}, 'pos'],
[{'accomplish': True}, 'pos'],
[{'accomplished': True}, 'pos'],
[{'accomplishment': True}, 'pos'],
[{'accomplishments': True}, 'pos'],
[{'accurate': True}, 'pos'],
[{'accurately': True}, 'pos'],
[{'achievable': True}, 'pos'],
[{'achievement': True}, 'pos'],
[{'achievements': True}, 'pos'],
[{'achievible': True}, 'pos'],
[{'acumen': True}, 'pos'],
[{'adaptable': True}, 'pos'],
[{'adaptive': True}, 'pos'],
[{'adequate': True}, 'pos'],
[{'adjustable': True}, 'pos'],
[{'admirable': True}, 'pos'],
[{'admirably': True}, 'pos'],
[{'admiration': True}, 'pos'],
[{'admire': True}, 'pos'],
[{'admirer': True}, 'pos'],
[{'admiring': True}, 'pos'],
[{'admiringly': True}, 'pos'],
[{'adorable': True}, 'pos'],
[{'adore': True}, 'pos'],
[{'adored': True}, 'pos'],
[{'adorer': True}, 'pos'],
[{'adoring': True}, 'pos'],
[{'adoringly': True}, 'pos'],
[{'adroit': True}, 'pos'],
[{'adroitly': True}, 'pos'],
[{'adulate': True}, 'pos'],
[{'adulation': True}, 'pos'],
[{'adulatory': True}, 'pos'],
[{'advanced': True}, 'pos'],
[{'advantage': True}, 'pos'],
[{'advantageous': True}, 'pos'],
[{'advantageously': True}, 'pos'],
[{'advantages': True}, 'pos'],
[{'adventuresome': True}, 'pos'],
[{'adventurous': True}, 'pos'],
[{'advocate': True}, 'pos'],
[{'advocated': True}, 'pos'],
[{'advocates': True}, 'pos'],
[{'affability': True}, 'pos'],
[{'affable': True}, 'pos'],
[{'affably': True}, 'pos'],
[{'affectation': True}, 'pos'],
[{'affection': True}, 'pos'],
[{'affectionate': True}, 'pos'],
[{'affinity': True}, 'pos'],
[{'affirm': True}, 'pos'],
[{'affirmation': True}, 'pos'],
[{'affirmative': True}, 'pos'],
[{'affluence': True}, 'pos'],
[{'affluent': True}, 'pos'],
[{'afford': True}, 'pos'],
[{'affordable': True}, 'pos'],
[{'affordably': True}, 'pos'],
[{'afordable': True}, 'pos'],
[{'agile': True}, 'pos'],
[{'agilely': True}, 'pos'],
[{'agility': True}, 'pos'],
[{'agreeable': True}, 'pos'],
[{'agreeableness': True}, 'pos'],
[{'agreeably': True}, 'pos'],
[{'all-around': True}, 'pos'],
[{'alluring': True}, 'pos'],
[{'alluringly': True}, 'pos'],
[{'altruistic': True}, 'pos'],
[{'altruistically': True}, 'pos'],
[{'amaze': True}, 'pos'],
[{'amazed': True}, 'pos'],
[{'amazement': True}, 'pos'],
[{'amazes': True}, 'pos'],
[{'amazing': True}, 'pos'],
[{'amazingly': True}, 'pos'],
[{'ambitious': True}, 'pos'],
[{'ambitiously': True}, 'pos'],
[{'ameliorate': True}, 'pos'],
[{'amenable': True}, 'pos'],
[{'amenity': True}, 'pos'],
[{'amiability': True}, 'pos'],
[{'amiabily': True}, 'pos'],
[{'amiable': True}, 'pos'],
[{'amicability': True}, 'pos'],
[{'amicable': True}, 'pos'],
[{'amicably': True}, 'pos'],
[{'amity': True}, 'pos'],
[{'ample': True}, 'pos'],
[{'amply': True}, 'pos'],
[{'amuse': True}, 'pos'],
[{'amusing': True}, 'pos'],
[{'amusingly': True}, 'pos'],
[{'angel': True}, 'pos'],
[{'angelic': True}, 'pos'],
[{'apotheosis': True}, 'pos'],
[{'appeal': True}, 'pos'],
[{'appealing': True}, 'pos'],
[{'applaud': True}, 'pos'],
[{'appreciable': True}, 'pos'],
[{'appreciate': True}, 'pos'],
[{'appreciated': True}, 'pos'],
[{'appreciates': True}, 'pos'],
[{'appreciative': True}, 'pos'],
[{'appreciatively': True}, 'pos'],
[{'appropriate': True}, 'pos'],
[{'approval': True}, 'pos'],
[{'approve': True}, 'pos'],
[{'ardent': True}, 'pos'],
[{'ardently': True}, 'pos'],
[{'ardor': True}, 'pos'],
[{'articulate': True}, 'pos'],
[{'aspiration': True}, 'pos'],
[{'aspirations': True}, 'pos'],
[{'aspire': True}, 'pos'],
[{'assurance': True}, 'pos'],
[{'assurances': True}, 'pos'],
[{'assure': True}, 'pos'],
[{'assuredly': True}, 'pos'],
[{'assuring': True}, 'pos'],
[{'astonish': True}, 'pos'],
[{'astonished': True}, 'pos'],
[{'astonishing': True}, 'pos'],
[{'astonishingly': True}, 'pos'],
[{'astonishment': True}, 'pos'],
[{'astound': True}, 'pos'],
[{'astounded': True}, 'pos'],
[{'astounding': True}, 'pos'],
[{'astoundingly': True}, 'pos'],
[{'astutely': True}, 'pos'],
[{'attentive': True}, 'pos'],
[{'attraction': True}, 'pos'],
[{'attractive': True}, 'pos'],
[{'attractively': True}, 'pos'],
[{'attune': True}, 'pos'],
[{'audible': True}, 'pos'],
[{'audibly': True}, 'pos'],
[{'auspicious': True}, 'pos'],
[{'authentic': True}, 'pos'],
[{'authoritative': True}, 'pos'],
[{'autonomous': True}, 'pos'],
[{'available': True}, 'pos'],
[{'aver': True}, 'pos'],
[{'avid': True}, 'pos'],
[{'avidly': True}, 'pos'],
[{'award': True}, 'pos'],
[{'awarded': True}, 'pos'],
[{'awards': True}, 'pos'],
[{'awe': True}, 'pos'],
[{'awed': True}, 'pos'],
[{'awesome': True}, 'pos'],
[{'awesomely': True}, 'pos'],
[{'awesomeness': True}, 'pos'],
[{'awestruck': True}, 'pos'],
[{'awsome': True}, 'pos'],
[{'backbone': True}, 'pos'],
[{'balanced': True}, 'pos'],
[{'bargain': True}, 'pos'],
[{'beauteous': True}, 'pos'],
[{'beautiful': True}, 'pos'],
[{'beautifullly': True}, 'pos'],
[{'beautifully': True}, 'pos'],
[{'beautify': True}, 'pos'],
[{'beauty': True}, 'pos'],
[{'beckon': True}, 'pos'],
[{'beckoned': True}, 'pos'],
[{'beckoning': True}, 'pos'],
[{'beckons': True}, 'pos'],
[{'believable': True}, 'pos'],
[{'believeable': True}, 'pos'],
[{'beloved': True}, 'pos'],
[{'benefactor': True}, 'pos'],
[{'beneficent': True}, 'pos'],
[{'beneficial': True}, 'pos'],
[{'beneficially': True}, 'pos'],
[{'beneficiary': True}, 'pos'],
[{'benefit': True}, 'pos'],
[{'benefits': True}, 'pos'],
[{'benevolence': True}, 'pos'],
[{'benevolent': True}, 'pos'],
[{'benifits': True}, 'pos'],
[{'best': True}, 'pos'],
[{'best-known': True}, 'pos'],
[{'best-performing': True}, 'pos'],
[{'best-selling': True}, 'pos'],
[{'better': True}, 'pos'],
[{'better-known': True}, 'pos'],
[{'better-than-expected': True}, 'pos'],
[{'beutifully': True}, 'pos'],
[{'blameless': True}, 'pos'],
[{'bless': True}, 'pos'],
[{'blessing': True}, 'pos'],
[{'bliss': True}, 'pos'],
[{'blissful': True}, 'pos'],
[{'blissfully': True}, 'pos'],
[{'blithe': True}, 'pos'],
[{'blockbuster': True}, 'pos'],
[{'bloom': True}, 'pos'],
[{'blossom': True}, 'pos'],
[{'bolster': True}, 'pos'],
[{'bonny': True}, 'pos'],
[{'bonus': True}, 'pos'],
[{'bonuses': True}, 'pos'],
[{'boom': True}, 'pos'],
[{'booming': True}, 'pos'],
[{'boost': True}, 'pos'],
[{'boundless': True}, 'pos'],
[{'bountiful': True}, 'pos'],
[{'brainiest': True}, 'pos'],
[{'brainy': True}, 'pos'],
[{'brand-new': True}, 'pos'],
[{'brave': True}, 'pos'],
[{'bravery': True}, 'pos'],
[{'bravo': True}, 'pos'],
[{'breakthrough': True}, 'pos'],
[{'breakthroughs': True}, 'pos'],
[{'breathlessness': True}, 'pos'],
[{'breathtaking': True}, 'pos'],
[{'breathtakingly': True}, 'pos'],
[{'breeze': True}, 'pos'],
[{'bright': True}, 'pos'],
[{'brighten': True}, 'pos'],
[{'brighter': True}, 'pos'],
[{'brightest': True}, 'pos'],
[{'brilliance': True}, 'pos'],
[{'brilliances': True}, 'pos'],
[{'brilliant': True}, 'pos'],
[{'brilliantly': True}, 'pos'],
[{'brisk': True}, 'pos'],
[{'brotherly': True}, 'pos'],
[{'bullish': True}, 'pos'],
[{'buoyant': True}, 'pos'],
[{'cajole': True}, 'pos'],
[{'calm': True}, 'pos'],
[{'calming': True}, 'pos'],
[{'calmness': True}, 'pos'],
[{'capability': True}, 'pos'],
[{'capable': True}, 'pos'],
[{'capably': True}, 'pos'],
[{'captivate': True}, 'pos'],
[{'captivating': True}, 'pos'],
[{'carefree': True}, 'pos'],
[{'cashback': True}, 'pos'],
[{'cashbacks': True}, 'pos'],
[{'catchy': True}, 'pos'],
[{'celebrate': True}, 'pos'],
[{'celebrated': True}, 'pos'],
[{'celebration': True}, 'pos'],
[{'celebratory': True}, 'pos'],
[{'champ': True}, 'pos'],
[{'champion': True}, 'pos'],
[{'charisma': True}, 'pos'],
[{'charismatic': True}, 'pos'],
[{'charitable': True}, 'pos'],
[{'charm': True}, 'pos'],
[{'charming': True}, 'pos'],
[{'charmingly': True}, 'pos'],
[{'chaste': True}, 'pos'],
[{'cheaper': True}, 'pos'],
[{'cheapest': True}, 'pos'],
[{'cheer': True}, 'pos'],
[{'cheerful': True}, 'pos'],
[{'cheery': True}, 'pos'],
[{'cherish': True}, 'pos'],
[{'cherished': True}, 'pos'],
[{'cherub': True}, 'pos'],
[{'chic': True}, 'pos'],
[{'chivalrous': True}, 'pos'],
[{'chivalry': True}, 'pos'],
[{'civility': True}, 'pos'],
[{'civilize': True}, 'pos'],
[{'clarity': True}, 'pos'],
[{'classic': True}, 'pos'],
[{'classy': True}, 'pos'],
[{'clean': True}, 'pos'],
[{'cleaner': True}, 'pos'],
[{'cleanest': True}, 'pos'],
[{'cleanliness': True}, 'pos'],
[{'cleanly': True}, 'pos'],
[{'clear': True}, 'pos'],
[{'clear-cut': True}, 'pos'],
[{'cleared': True}, 'pos'],
[{'clearer': True}, 'pos'],
[{'clearly': True}, 'pos'],
[{'clears': True}, 'pos'],
[{'clever': True}, 'pos'],
[{'cleverly': True}, 'pos'],
[{'cohere': True}, 'pos'],
[{'coherence': True}, 'pos'],
[{'coherent': True}, 'pos'],
[{'cohesive': True}, 'pos'],
[{'colorful': True}, 'pos'],
[{'comely': True}, 'pos'],
[{'comfort': True}, 'pos'],
[{'comfortable': True}, 'pos'],
[{'comfortably': True}, 'pos'],
[{'comforting': True}, 'pos'],
[{'comfy': True}, 'pos'],
[{'commend': True}, 'pos'],
[{'commendable': True}, 'pos'],
[{'commendably': True}, 'pos'],
[{'commitment': True}, 'pos'],
[{'commodious': True}, 'pos'],
[{'compact': True}, 'pos'],
[{'compactly': True}, 'pos'],
[{'compassion': True}, 'pos'],
[{'compassionate': True}, 'pos'],
[{'compatible': True}, 'pos'],
[{'competitive': True}, 'pos'],
[{'complement': True}, 'pos'],
[{'complementary': True}, 'pos'],
[{'complemented': True}, 'pos'],
[{'complements': True}, 'pos'],
[{'compliant': True}, 'pos'],
[{'compliment': True}, 'pos'],
[{'complimentary': True}, 'pos'],
[{'comprehensive': True}, 'pos'],
[{'conciliate': True}, 'pos'],
[{'conciliatory': True}, 'pos'],
[{'concise': True}, 'pos'],
[{'confidence': True}, 'pos'],
[{'confident': True}, 'pos'],
[{'congenial': True}, 'pos'],
[{'congratulate': True}, 'pos'],
[{'congratulation': True}, 'pos'],
[{'congratulations': True}, 'pos'],
[{'congratulatory': True}, 'pos'],
[{'conscientious': True}, 'pos'],
[{'considerate': True}, 'pos'],
[{'consistent': True}, 'pos'],
[{'consistently': True}, 'pos'],
[{'constructive': True}, 'pos'],
[{'consummate': True}, 'pos'],
[{'contentment': True}, 'pos'],
[{'continuity': True}, 'pos'],
[{'contrasty': True}, 'pos'],
[{'contribution': True}, 'pos'],
[{'convenience': True}, 'pos'],
[{'convenient': True}, 'pos'],
[{'conveniently': True}, 'pos'],
[{'convience': True}, 'pos'],
[{'convienient': True}, 'pos'],
[{'convient': True}, 'pos'],
[{'convincing': True}, 'pos'],
[{'convincingly': True}, 'pos'],
[{'cool': True}, 'pos'],
[{'coolest': True}, 'pos'],
[{'cooperative': True}, 'pos'],
[{'cooperatively': True}, 'pos'],
[{'cornerstone': True}, 'pos'],
[{'correct': True}, 'pos'],
[{'correctly': True}, 'pos'],
[{'cost-effective': True}, 'pos'],
[{'cost-saving': True}, 'pos'],
[{'counter-attack': True}, 'pos'],
[{'counter-attacks': True}, 'pos'],
[{'courage': True}, 'pos'],
[{'courageous': True}, 'pos'],
[{'courageously': True}, 'pos'],
[{'courageousness': True}, 'pos'],
[{'courteous': True}, 'pos'],
[{'courtly': True}, 'pos'],
[{'covenant': True}, 'pos'],
[{'cozy': True}, 'pos'],
[{'creative': True}, 'pos'],
[{'credence': True}, 'pos'],
[{'credible': True}, 'pos'],
[{'crisp': True}, 'pos'],
[{'crisper': True}, 'pos'],
[{'cure': True}, 'pos'],
[{'cure-all': True}, 'pos'],
[{'cushy': True}, 'pos'],
[{'cute': True}, 'pos'],
[{'cuteness': True}, 'pos'],
[{'danke': True}, 'pos'],
[{'danken': True}, 'pos'],
[{'daring': True}, 'pos'],
[{'daringly': True}, 'pos'],
[{'darling': True}, 'pos'],
[{'dashing': True}, 'pos'],
[{'dauntless': True}, 'pos'],
[{'dawn': True}, 'pos'],
[{'dazzle': True}, 'pos'],
[{'dazzled': True}, 'pos'],
[{'dazzling': True}, 'pos'],
[{'dead-cheap': True}, 'pos'],
[{'dead-on': True}, 'pos'],
[{'decency': True}, 'pos'],
[{'decent': True}, 'pos'],
[{'decisive': True}, 'pos'],
[{'decisiveness': True}, 'pos'],
[{'dedicated': True}, 'pos'],
[{'defeat': True}, 'pos'],
[{'defeated': True}, 'pos'],
[{'defeating': True}, 'pos'],
[{'defeats': True}, 'pos'],
[{'defender': True}, 'pos'],
[{'deference': True}, 'pos'],
[{'deft': True}, 'pos'],
[{'deginified': True}, 'pos'],
[{'delectable': True}, 'pos'],
[{'delicacy': True}, 'pos'],
[{'delicate': True}, 'pos'],
[{'delicious': True}, 'pos'],
[{'delight': True}, 'pos'],
[{'delighted': True}, 'pos'],
[{'delightful': True}, 'pos'],
[{'delightfully': True}, 'pos'],
[{'delightfulness': True}, 'pos'],
[{'dependable': True}, 'pos'],
[{'dependably': True}, 'pos'],
[{'deservedly': True}, 'pos'],
[{'deserving': True}, 'pos'],
[{'desirable': True}, 'pos'],
[{'desiring': True}, 'pos'],
[{'desirous': True}, 'pos'],
[{'destiny': True}, 'pos'],
[{'detachable': True}, 'pos'],
[{'devout': True}, 'pos'],
[{'dexterous': True}, 'pos'],
[{'dexterously': True}, 'pos'],
[{'dextrous': True}, 'pos'],
[{'dignified': True}, 'pos'],
[{'dignify': True}, 'pos'],
[{'dignity': True}, 'pos'],
[{'diligence': True}, 'pos'],
[{'diligent': True}, 'pos'],
[{'diligently': True}, 'pos'],
[{'diplomatic': True}, 'pos'],
[{'dirt-cheap': True}, 'pos'],
[{'distinction': True}, 'pos'],
[{'distinctive': True}, 'pos'],
[{'distinguished': True}, 'pos'],
[{'diversified': True}, 'pos'],
[{'divine': True}, 'pos'],
[{'divinely': True}, 'pos'],
[{'dominate': True}, 'pos'],
[{'dominated': True}, 'pos'],
[{'dominates': True}, 'pos'],
[{'dote': True}, 'pos'],
[{'dotingly': True}, 'pos'],
[{'doubtless': True}, 'pos'],
[{'dreamland': True}, 'pos'],
[{'dumbfounded': True}, 'pos'],
[{'dumbfounding': True}, 'pos'],
[{'dummy-proof': True}, 'pos'],
[{'durable': True}, 'pos'],
[{'dynamic': True}, 'pos'],
[{'eager': True}, 'pos'],
[{'eagerly': True}, 'pos'],
[{'eagerness': True}, 'pos'],
[{'earnest': True}, 'pos'],
[{'earnestly': True}, 'pos'],
[{'earnestness': True}, 'pos'],
[{'ease': True}, 'pos'],
[{'eased': True}, 'pos'],
[{'eases': True}, 'pos'],
[{'easier': True}, 'pos'],
[{'easiest': True}, 'pos'],
[{'easiness': True}, 'pos'],
[{'easing': True}, 'pos'],
[{'easy': True}, 'pos'],
[{'easy-to-use': True}, 'pos'],
[{'easygoing': True}, 'pos'],
[{'ebullience': True}, 'pos'],
[{'ebullient': True}, 'pos'],
[{'ebulliently': True}, 'pos'],
[{'ecenomical': True}, 'pos'],
[{'economical': True}, 'pos'],
[{'ecstasies': True}, 'pos'],
[{'ecstasy': True}, 'pos'],
[{'ecstatic': True}, 'pos'],
[{'ecstatically': True}, 'pos'],
[{'edify': True}, 'pos'],
[{'educated': True}, 'pos'],
[{'effective': True}, 'pos'],
[{'effectively': True}, 'pos'],
[{'effectiveness': True}, 'pos'],
[{'effectual': True}, 'pos'],
[{'efficacious': True}, 'pos'],
[{'efficient': True}, 'pos'],
[{'efficiently': True}, 'pos'],
[{'effortless': True}, 'pos'],
[{'effortlessly': True}, 'pos'],
[{'effusion': True}, 'pos'],
[{'effusive': True}, 'pos'],
[{'effusively': True}, 'pos'],
[{'effusiveness': True}, 'pos'],
[{'elan': True}, 'pos'],
[{'elate': True}, 'pos'],
[{'elated': True}, 'pos'],
[{'elatedly': True}, 'pos'],
[{'elation': True}, 'pos'],
[{'electrify': True}, 'pos'],
[{'elegance': True}, 'pos'],
[{'elegant': True}, 'pos'],
[{'elegantly': True}, 'pos'],
[{'elevate': True}, 'pos'],
[{'elite': True}, 'pos'],
[{'eloquence': True}, 'pos'],
[{'eloquent': True}, 'pos'],
[{'eloquently': True}, 'pos'],
[{'embolden': True}, 'pos'],
[{'eminence': True}, 'pos'],
[{'eminent': True}, 'pos'],
[{'empathize': True}, 'pos'],
[{'empathy': True}, 'pos'],
[{'empower': True}, 'pos'],
[{'empowerment': True}, 'pos'],
[{'enchant': True}, 'pos'],
[{'enchanted': True}, 'pos'],
[{'enchanting': True}, 'pos'],
[{'enchantingly': True}, 'pos'],
[{'encourage': True}, 'pos'],
[{'encouragement': True}, 'pos'],
[{'encouraging': True}, 'pos'],
[{'encouragingly': True}, 'pos'],
[{'endear': True}, 'pos'],
[{'endearing': True}, 'pos'],
[{'endorse': True}, 'pos'],
[{'endorsed': True}, 'pos'],
[{'endorsement': True}, 'pos'],
[{'endorses': True}, 'pos'],
[{'endorsing': True}, 'pos'],
[{'energetic': True}, 'pos'],
[{'energize': True}, 'pos'],
[{'energy-efficient': True}, 'pos'],
[{'energy-saving': True}, 'pos'],
[{'engaging': True}, 'pos'],
[{'engrossing': True}, 'pos'],
[{'enhance': True}, 'pos'],
[{'enhanced': True}, 'pos'],
[{'enhancement': True}, 'pos'],
[{'enhances': True}, 'pos'],
[{'enjoy': True}, 'pos'],
[{'enjoyable': True}, 'pos'],
[{'enjoyably': True}, 'pos'],
[{'enjoyed': True}, 'pos'],
[{'enjoying': True}, 'pos'],
[{'enjoyment': True}, 'pos'],
[{'enjoys': True}, 'pos'],
[{'enlighten': True}, 'pos'],
[{'enlightenment': True}, 'pos'],
[{'enliven': True}, 'pos'],
[{'ennoble': True}, 'pos'],
[{'enough': True}, 'pos'],
[{'enrapt': True}, 'pos'],
[{'enrapture': True}, 'pos'],
[{'enraptured': True}, 'pos'],
[{'enrich': True}, 'pos'],
[{'enrichment': True}, 'pos'],
[{'enterprising': True}, 'pos'],
[{'entertain': True}, 'pos'],
[{'entertaining': True}, 'pos'],
[{'entertains': True}, 'pos'],
[{'enthral': True}, 'pos'],
[{'enthrall': True}, 'pos'],
[{'enthralled': True}, 'pos'],
[{'enthuse': True}, 'pos'],
[{'enthusiasm': True}, 'pos'],
[{'enthusiast': True}, 'pos'],
[{'enthusiastic': True}, 'pos'],
[{'enthusiastically': True}, 'pos'],
[{'entice': True}, 'pos'],
[{'enticed': True}, 'pos'],
[{'enticing': True}, 'pos'],
[{'enticingly': True}, 'pos'],
[{'entranced': True}, 'pos'],
[{'entrancing': True}, 'pos'],
[{'entrust': True}, 'pos'],
[{'enviable': True}, 'pos'],
[{'enviably': True}, 'pos'],
[{'envious': True}, 'pos'],
[{'enviously': True}, 'pos'],
[{'enviousness': True}, 'pos'],
[{'envy': True}, 'pos'],
[{'equitable': True}, 'pos'],
[{'ergonomical': True}, 'pos'],
[{'err-free': True}, 'pos'],
[{'erudite': True}, 'pos'],
[{'ethical': True}, 'pos'],
[{'eulogize': True}, 'pos'],
[{'euphoria': True}, 'pos'],
[{'euphoric': True}, 'pos'],
[{'euphorically': True}, 'pos'],
[{'evaluative': True}, 'pos'],
[{'evenly': True}, 'pos'],
[{'eventful': True}, 'pos'],
[{'everlasting': True}, 'pos'],
[{'evocative': True}, 'pos'],
[{'exalt': True}, 'pos'],
[{'exaltation': True}, 'pos'],
[{'exalted': True}, 'pos'],
[{'exaltedly': True}, 'pos'],
[{'exalting': True}, 'pos'],
[{'exaltingly': True}, 'pos'],
[{'examplar': True}, 'pos'],
[{'examplary': True}, 'pos'],
[{'excallent': True}, 'pos'],
[{'exceed': True}, 'pos'],
[{'exceeded': True}, 'pos'],
[{'exceeding': True}, 'pos'],
[{'exceedingly': True}, 'pos'],
[{'exceeds': True}, 'pos'],
[{'excel': True}, 'pos'],
[{'exceled': True}, 'pos'],
[{'excelent': True}, 'pos'],
[{'excellant': True}, 'pos'],
[{'excelled': True}, 'pos'],
[{'excellence': True}, 'pos'],
[{'excellency': True}, 'pos'],
[{'excellent': True}, 'pos'],
[{'excellently': True}, 'pos'],
[{'excels': True}, 'pos'],
[{'exceptional': True}, 'pos'],
[{'exceptionally': True}, 'pos'],
[{'excite': True}, 'pos'],
[{'excited': True}, 'pos'],
[{'excitedly': True}, 'pos'],
[{'excitedness': True}, 'pos'],
[{'excitement': True}, 'pos'],
[{'excites': True}, 'pos'],
[{'exciting': True}, 'pos'],
[{'excitingly': True}, 'pos'],
[{'exellent': True}, 'pos'],
[{'exemplar': True}, 'pos'],
[{'exemplary': True}, 'pos'],
[{'exhilarate': True}, 'pos'],
[{'exhilarating': True}, 'pos'],
[{'exhilaratingly': True}, 'pos'],
[{'exhilaration': True}, 'pos'],
[{'exonerate': True}, 'pos'],
[{'expansive': True}, 'pos'],
[{'expeditiously': True}, 'pos'],
[{'expertly': True}, 'pos'],
[{'exquisite': True}, 'pos'],
[{'exquisitely': True}, 'pos'],
[{'extol': True}, 'pos'],
[{'extoll': True}, 'pos'],
[{'extraordinarily': True}, 'pos'],
[{'extraordinary': True}, 'pos'],
[{'exuberance': True}, 'pos'],
[{'exuberant': True}, 'pos'],
[{'exuberantly': True}, 'pos'],
[{'exult': True}, 'pos'],
[{'exultant': True}, 'pos'],
[{'exultation': True}, 'pos'],
[{'exultingly': True}, 'pos'],
[{'eye-catch': True}, 'pos'],
[{'eye-catching': True}, 'pos'],
[{'eyecatch': True}, 'pos'],
[{'eyecatching': True}, 'pos'],
[{'fabulous': True}, 'pos'],
[{'fabulously': True}, 'pos'],
[{'facilitate': True}, 'pos'],
[{'fair': True}, 'pos'],
[{'fairly': True}, 'pos'],
[{'fairness': True}, 'pos'],
[{'faith': True}, 'pos'],
[{'faithful': True}, 'pos'],
[{'faithfully': True}, 'pos'],
[{'faithfulness': True}, 'pos'],
[{'fame': True}, 'pos'],
[{'famed': True}, 'pos'],
[{'famous': True}, 'pos'],
[{'famously': True}, 'pos'],
[{'fancier': True}, 'pos'],
[{'fancinating': True}, 'pos'],
[{'fancy': True}, 'pos'],
[{'fanfare': True}, 'pos'],
[{'fans': True}, 'pos'],
[{'fantastic': True}, 'pos'],
[{'fantastically': True}, 'pos'],
[{'fascinate': True}, 'pos'],
[{'fascinating': True}, 'pos'],
[{'fascinatingly': True}, 'pos'],
[{'fascination': True}, 'pos'],
[{'fashionable': True}, 'pos'],
[{'fashionably': True}, 'pos'],
[{'fast': True}, 'pos'],
[{'fast-growing': True}, 'pos'],
[{'fast-paced': True}, 'pos'],
[{'faster': True}, 'pos'],
[{'fastest': True}, 'pos'],
[{'fastest-growing': True}, 'pos'],
[{'faultless': True}, 'pos'],
[{'fav': True}, 'pos'],
[{'fave': True}, 'pos'],
[{'favor': True}, 'pos'],
[{'favorable': True}, 'pos'],
[{'favored': True}, 'pos'],
[{'favorite': True}, 'pos'],
[{'favorited': True}, 'pos'],
[{'favour': True}, 'pos'],
[{'fearless': True}, 'pos'],
[{'fearlessly': True}, 'pos'],
[{'feasible': True}, 'pos'],
[{'feasibly': True}, 'pos'],
[{'feat': True}, 'pos'],
[{'feature-rich': True}, 'pos'],
[{'fecilitous': True}, 'pos'],
[{'feisty': True}, 'pos'],
[{'felicitate': True}, 'pos'],
[{'felicitous': True}, 'pos'],
[{'felicity': True}, 'pos'],
[{'fertile': True}, 'pos'],
[{'fervent': True}, 'pos'],
[{'fervently': True}, 'pos'],
[{'fervid': True}, 'pos'],
[{'fervidly': True}, 'pos'],
[{'fervor': True}, 'pos'],
[{'festive': True}, 'pos'],
[{'fidelity': True}, 'pos'],
[{'fiery': True}, 'pos'],
[{'fine': True}, 'pos'],
[{'fine-looking': True}, 'pos'],
[{'finely': True}, 'pos'],
[{'finer': True}, 'pos'],
[{'finest': True}, 'pos'],
[{'firmer': True}, 'pos'],
[{'first-class': True}, 'pos'],
[{'first-in-class': True}, 'pos'],
[{'first-rate': True}, 'pos'],
[{'flashy': True}, 'pos'],
[{'flatter': True}, 'pos'],
[{'flattering': True}, 'pos'],
[{'flatteringly': True}, 'pos'],
[{'flawless': True}, 'pos'],
[{'flawlessly': True}, 'pos'],
[{'flexibility': True}, 'pos'],
[{'flexible': True}, 'pos'],
[{'flourish': True}, 'pos'],
[{'flourishing': True}, 'pos'],
[{'fluent': True}, 'pos'],
[{'flutter': True}, 'pos'],
[{'fond': True}, 'pos'],
[{'fondly': True}, 'pos'],
[{'fondness': True}, 'pos'],
[{'foolproof': True}, 'pos'],
[{'foremost': True}, 'pos'],
[{'foresight': True}, 'pos'],
[{'formidable': True}, 'pos'],
[{'fortitude': True}, 'pos'],
[{'fortuitous': True}, 'pos'],
[{'fortuitously': True}, 'pos'],
[{'fortunate': True}, 'pos'],
[{'fortunately': True}, 'pos'],
[{'fortune': True}, 'pos'],
[{'fragrant': True}, 'pos'],
[{'free': True}, 'pos'],
[{'freed': True}, 'pos'],
[{'freedom': True}, 'pos'],
[{'freedoms': True}, 'pos'],
[{'fresh': True}, 'pos'],
[{'fresher': True}, 'pos'],
[{'freshest': True}, 'pos'],
[{'friendliness': True}, 'pos'],
[{'friendly': True}, 'pos'],
[{'frolic': True}, 'pos'],
[{'frugal': True}, 'pos'],
[{'fruitful': True}, 'pos'],
[{'ftw': True}, 'pos'],
[{'fulfillment': True}, 'pos'],
[{'fun': True}, 'pos'],
[{'futurestic': True}, 'pos'],
[{'futuristic': True}, 'pos'],
[{'gaiety': True}, 'pos'],
[{'gaily': True}, 'pos'],
[{'gain': True}, 'pos'],
[{'gained': True}, 'pos'],
[{'gainful': True}, 'pos'],
[{'gainfully': True}, 'pos'],
[{'gaining': True}, 'pos'],
[{'gains': True}, 'pos'],
[{'gallant': True}, 'pos'],
[{'gallantly': True}, 'pos'],
[{'galore': True}, 'pos'],
[{'geekier': True}, 'pos'],
[{'geeky': True}, 'pos'],
[{'gem': True}, 'pos'],
[{'gems': True}, 'pos'],
[{'generosity': True}, 'pos'],
[{'generous': True}, 'pos'],
[{'generously': True}, 'pos'],
[{'genial': True}, 'pos'],
[{'genius': True}, 'pos'],
[{'gentle': True}, 'pos'],
[{'gentlest': True}, 'pos'],
[{'genuine': True}, 'pos'],
[{'gifted': True}, 'pos'],
[{'glad': True}, 'pos'],
[{'gladden': True}, 'pos'],
[{'gladly': True}, 'pos'],
[{'gladness': True}, 'pos'],
[{'glamorous': True}, 'pos'],
[{'glee': True}, 'pos'],
[{'gleeful': True}, 'pos'],
[{'gleefully': True}, 'pos'],
[{'glimmer': True}, 'pos'],
[{'glimmering': True}, 'pos'],
[{'glisten': True}, 'pos'],
[{'glistening': True}, 'pos'],
[{'glitter': True}, 'pos'],
[{'glitz': True}, 'pos'],
[{'glorify': True}, 'pos'],
[{'glorious': True}, 'pos'],
[{'gloriously': True}, 'pos'],
[{'glory': True}, 'pos'],
[{'glow': True}, 'pos'],
[{'glowing': True}, 'pos'],
[{'glowingly': True}, 'pos'],
[{'god-given': True}, 'pos'],
[{'god-send': True}, 'pos'],
[{'godlike': True}, 'pos'],
[{'godsend': True}, 'pos'],
[{'gold': True}, 'pos'],
[{'golden': True}, 'pos'],
[{'good': True}, 'pos'],
[{'goodly': True}, 'pos'],
[{'goodness': True}, 'pos'],
[{'goodwill': True}, 'pos'],
[{'goood': True}, 'pos'],
[{'gooood': True}, 'pos'],
[{'gorgeous': True}, 'pos'],
[{'gorgeously': True}, 'pos'],
[{'grace': True}, 'pos'],
[{'graceful': True}, 'pos'],
[{'gracefully': True}, 'pos'],
[{'gracious': True}, 'pos'],
[{'graciously': True}, 'pos'],
[{'graciousness': True}, 'pos'],
[{'grand': True}, 'pos'],
[{'grandeur': True}, 'pos'],
[{'grateful': True}, 'pos'],
[{'gratefully': True}, 'pos'],
[{'gratification': True}, 'pos'],
[{'gratified': True}, 'pos'],
[{'gratifies': True}, 'pos'],
[{'gratify': True}, 'pos'],
[{'gratifying': True}, 'pos'],
[{'gratifyingly': True}, 'pos'],
[{'gratitude': True}, 'pos'],
[{'great': True}, 'pos'],
[{'greatest': True}, 'pos'],
[{'greatness': True}, 'pos'],
[{'grin': True}, 'pos'],
[{'groundbreaking': True}, 'pos'],
[{'guarantee': True}, 'pos'],
[{'guidance': True}, 'pos'],
[{'guiltless': True}, 'pos'],
[{'gumption': True}, 'pos'],
[{'gush': True}, 'pos'],
[{'gusto': True}, 'pos'],
[{'gutsy': True}, 'pos'],
[{'hail': True}, 'pos'],
[{'halcyon': True}, 'pos'],
[{'hale': True}, 'pos'],
[{'hallmark': True}, 'pos'],
[{'hallmarks': True}, 'pos'],
[{'hallowed': True}, 'pos'],
[{'handier': True}, 'pos'],
[{'handily': True}, 'pos'],
[{'hands-down': True}, 'pos'],
[{'handsome': True}, 'pos'],
[{'handsomely': True}, 'pos'],
[{'handy': True}, 'pos'],
[{'happier': True}, 'pos'],
[{'happily': True}, 'pos'],
[{'happiness': True}, 'pos'],
[{'happy': True}, 'pos'],
[{'hard-working': True}, 'pos'],
[{'hardier': True}, 'pos'],
[{'hardy': True}, 'pos'],
[{'harmless': True}, 'pos'],
[{'harmonious': True}, 'pos'],
[{'harmoniously': True}, 'pos'],
[{'harmonize': True}, 'pos'],
[{'harmony': True}, 'pos'],
[{'headway': True}, 'pos'],
[{'heal': True}, 'pos'],
[{'healthful': True}, 'pos'],
[{'healthy': True}, 'pos'],
[{'hearten': True}, 'pos'],
[{'heartening': True}, 'pos'],
[{'heartfelt': True}, 'pos'],
[{'heartily': True}, 'pos'],
[{'heartwarming': True}, 'pos'],
[{'heaven': True}, 'pos'],
[{'heavenly': True}, 'pos'],
[{'helped': True}, 'pos'],
[{'helpful': True}, 'pos'],
[{'helping': True}, 'pos'],
[{'hero': True}, 'pos'],
[{'heroic': True}, 'pos'],
[{'heroically': True}, 'pos'],
[{'heroine': True}, 'pos'],
[{'heroize': True}, 'pos'],
[{'heros': True}, 'pos'],
[{'high-quality': True}, 'pos'],
[{'high-spirited': True}, 'pos'],
[{'hilarious': True}, 'pos'],
[{'holy': True}, 'pos'],
[{'homage': True}, 'pos'],
[{'honest': True}, 'pos'],
[{'honesty': True}, 'pos'],
[{'honor': True}, 'pos'],
[{'honorable': True}, 'pos'],
[{'honored': True}, 'pos'],
[{'honoring': True}, 'pos'],
[{'hooray': True}, 'pos'],
[{'hopeful': True}, 'pos'],
[{'hospitable': True}, 'pos'],
[{'hot': True}, 'pos'],
[{'hotcake': True}, 'pos'],
[{'hotcakes': True}, 'pos'],
[{'hottest': True}, 'pos'],
[{'hug': True}, 'pos'],
[{'humane': True}, 'pos'],
[{'humble': True}, 'pos'],
[{'humility': True}, 'pos'],
[{'humor': True}, 'pos'],
[{'humorous': True}, 'pos'],
[{'humorously': True}, 'pos'],
[{'humour': True}, 'pos'],
[{'humourous': True}, 'pos'],
[{'ideal': True}, 'pos'],
[{'idealize': True}, 'pos'],
[{'ideally': True}, 'pos'],
[{'idol': True}, 'pos'],
[{'idolize': True}, 'pos'],
[{'idolized': True}, 'pos'],
[{'idyllic': True}, 'pos'],
[{'illuminate': True}, 'pos'],
[{'illuminati': True}, 'pos'],
[{'illuminating': True}, 'pos'],
[{'illumine': True}, 'pos'],
[{'illustrious': True}, 'pos'],
[{'ilu': True}, 'pos'],
[{'imaculate': True}, 'pos'],
[{'imaginative': True}, 'pos'],
[{'immaculate': True}, 'pos'],
[{'immaculately': True}, 'pos'],
[{'immense': True}, 'pos'],
[{'impartial': True}, 'pos'],
[{'impartiality': True}, 'pos'],
[{'impartially': True}, 'pos'],
[{'impassioned': True}, 'pos'],
[{'impeccable': True}, 'pos'],
[{'impeccably': True}, 'pos'],
[{'important': True}, 'pos'],
[{'impress': True}, 'pos'],
[{'impressed': True}, 'pos'],
[{'impresses': True}, 'pos'],
[{'impressive': True}, 'pos'],
[{'impressively': True}, 'pos'],
[{'impressiveness': True}, 'pos'],
[{'improve': True}, 'pos'],
[{'improved': True}, 'pos'],
[{'improvement': True}, 'pos'],
[{'improvements': True}, 'pos'],
[{'improves': True}, 'pos'],
[{'improving': True}, 'pos'],
[{'incredible': True}, 'pos'],
[{'incredibly': True}, 'pos'],
[{'indebted': True}, 'pos'],
[{'individualized': True}, 'pos'],
[{'indulgence': True}, 'pos'],
[{'indulgent': True}, 'pos'],
[{'industrious': True}, 'pos'],
[{'inestimable': True}, 'pos'],
[{'inestimably': True}, 'pos'],
[{'inexpensive': True}, 'pos'],
[{'infallibility': True}, 'pos'],
[{'infallible': True}, 'pos'],
[{'infallibly': True}, 'pos'],
[{'influential': True}, 'pos'],
[{'ingenious': True}, 'pos'],
[{'ingeniously': True}, 'pos'],
[{'ingenuity': True}, 'pos'],
[{'ingenuous': True}, 'pos'],
[{'ingenuously': True}, 'pos'],
[{'innocuous': True}, 'pos'],
[{'innovation': True}, 'pos'],
[{'innovative': True}, 'pos'],
[{'inpressed': True}, 'pos'],
[{'insightful': True}, 'pos'],
...]
In [58]:
with open('negative_words.txt','r',encoding='utf-8') as negFile:
lines = negFile.readlines()
for line in lines:
neg_features.append([make_full_dict(line.rstrip()),'neg'])
In [59]:
neg_features
Out[59]:
[[{'2-faced': True}, 'neg'],
[{'2-faces': True}, 'neg'],
[{'abnormal': True}, 'neg'],
[{'abolish': True}, 'neg'],
[{'abominable': True}, 'neg'],
[{'abominably': True}, 'neg'],
[{'abominate': True}, 'neg'],
[{'abomination': True}, 'neg'],
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[{'confusions': True}, 'neg'],
[{'congested': True}, 'neg'],
[{'congestion': True}, 'neg'],
[{'cons': True}, 'neg'],
[{'conscons': True}, 'neg'],
[{'conservative': True}, 'neg'],
[{'conspicuous': True}, 'neg'],
[{'conspicuously': True}, 'neg'],
[{'conspiracies': True}, 'neg'],
[{'conspiracy': True}, 'neg'],
[{'conspirator': True}, 'neg'],
[{'conspiratorial': True}, 'neg'],
[{'conspire': True}, 'neg'],
[{'consternation': True}, 'neg'],
[{'contagious': True}, 'neg'],
[{'contaminate': True}, 'neg'],
[{'contaminated': True}, 'neg'],
[{'contaminates': True}, 'neg'],
[{'contaminating': True}, 'neg'],
[{'contamination': True}, 'neg'],
[{'contempt': True}, 'neg'],
[{'contemptible': True}, 'neg'],
[{'contemptuous': True}, 'neg'],
[{'contemptuously': True}, 'neg'],
[{'contend': True}, 'neg'],
[{'contention': True}, 'neg'],
[{'contentious': True}, 'neg'],
[{'contort': True}, 'neg'],
[{'contortions': True}, 'neg'],
[{'contradict': True}, 'neg'],
[{'contradiction': True}, 'neg'],
[{'contradictory': True}, 'neg'],
[{'contrariness': True}, 'neg'],
[{'contravene': True}, 'neg'],
[{'contrive': True}, 'neg'],
[{'contrived': True}, 'neg'],
[{'controversial': True}, 'neg'],
[{'controversy': True}, 'neg'],
[{'convoluted': True}, 'neg'],
[{'corrode': True}, 'neg'],
[{'corrosion': True}, 'neg'],
[{'corrosions': True}, 'neg'],
[{'corrosive': True}, 'neg'],
[{'corrupt': True}, 'neg'],
[{'corrupted': True}, 'neg'],
[{'corrupting': True}, 'neg'],
[{'corruption': True}, 'neg'],
[{'corrupts': True}, 'neg'],
[{'corruptted': True}, 'neg'],
[{'costlier': True}, 'neg'],
[{'costly': True}, 'neg'],
[{'counter-productive': True}, 'neg'],
[{'counterproductive': True}, 'neg'],
[{'coupists': True}, 'neg'],
[{'covetous': True}, 'neg'],
[{'coward': True}, 'neg'],
[{'cowardly': True}, 'neg'],
[{'crabby': True}, 'neg'],
[{'crack': True}, 'neg'],
[{'cracked': True}, 'neg'],
[{'cracks': True}, 'neg'],
[{'craftily': True}, 'neg'],
[{'craftly': True}, 'neg'],
[{'crafty': True}, 'neg'],
[{'cramp': True}, 'neg'],
[{'cramped': True}, 'neg'],
[{'cramping': True}, 'neg'],
[{'cranky': True}, 'neg'],
[{'crap': True}, 'neg'],
[{'crappy': True}, 'neg'],
[{'craps': True}, 'neg'],
[{'crash': True}, 'neg'],
[{'crashed': True}, 'neg'],
[{'crashes': True}, 'neg'],
[{'crashing': True}, 'neg'],
[{'crass': True}, 'neg'],
[{'craven': True}, 'neg'],
[{'cravenly': True}, 'neg'],
[{'craze': True}, 'neg'],
[{'crazily': True}, 'neg'],
[{'craziness': True}, 'neg'],
[{'crazy': True}, 'neg'],
[{'creak': True}, 'neg'],
[{'creaking': True}, 'neg'],
[{'creaks': True}, 'neg'],
[{'credulous': True}, 'neg'],
[{'creep': True}, 'neg'],
[{'creeping': True}, 'neg'],
[{'creeps': True}, 'neg'],
[{'creepy': True}, 'neg'],
[{'crept': True}, 'neg'],
[{'crime': True}, 'neg'],
[{'criminal': True}, 'neg'],
[{'cringe': True}, 'neg'],
[{'cringed': True}, 'neg'],
[{'cringes': True}, 'neg'],
[{'cripple': True}, 'neg'],
[{'crippled': True}, 'neg'],
[{'cripples': True}, 'neg'],
[{'crippling': True}, 'neg'],
[{'crisis': True}, 'neg'],
[{'critic': True}, 'neg'],
[{'critical': True}, 'neg'],
[{'criticism': True}, 'neg'],
[{'criticisms': True}, 'neg'],
[{'criticize': True}, 'neg'],
[{'criticized': True}, 'neg'],
[{'criticizing': True}, 'neg'],
[{'critics': True}, 'neg'],
[{'cronyism': True}, 'neg'],
[{'crook': True}, 'neg'],
[{'crooked': True}, 'neg'],
[{'crooks': True}, 'neg'],
[{'crowded': True}, 'neg'],
[{'crowdedness': True}, 'neg'],
[{'crude': True}, 'neg'],
[{'cruel': True}, 'neg'],
[{'crueler': True}, 'neg'],
[{'cruelest': True}, 'neg'],
[{'cruelly': True}, 'neg'],
[{'cruelness': True}, 'neg'],
[{'cruelties': True}, 'neg'],
[{'cruelty': True}, 'neg'],
[{'crumble': True}, 'neg'],
[{'crumbling': True}, 'neg'],
[{'crummy': True}, 'neg'],
[{'crumple': True}, 'neg'],
[{'crumpled': True}, 'neg'],
[{'crumples': True}, 'neg'],
[{'crush': True}, 'neg'],
[{'crushed': True}, 'neg'],
[{'crushing': True}, 'neg'],
[{'cry': True}, 'neg'],
[{'culpable': True}, 'neg'],
[{'culprit': True}, 'neg'],
[{'cumbersome': True}, 'neg'],
[{'cunt': True}, 'neg'],
[{'cunts': True}, 'neg'],
[{'cuplrit': True}, 'neg'],
[{'curse': True}, 'neg'],
[{'cursed': True}, 'neg'],
[{'curses': True}, 'neg'],
[{'curt': True}, 'neg'],
[{'cuss': True}, 'neg'],
[{'cussed': True}, 'neg'],
[{'cutthroat': True}, 'neg'],
[{'cynical': True}, 'neg'],
[{'cynicism': True}, 'neg'],
[{'d*mn': True}, 'neg'],
[{'damage': True}, 'neg'],
[{'damaged': True}, 'neg'],
[{'damages': True}, 'neg'],
[{'damaging': True}, 'neg'],
[{'damn': True}, 'neg'],
[{'damnable': True}, 'neg'],
[{'damnably': True}, 'neg'],
[{'damnation': True}, 'neg'],
[{'damned': True}, 'neg'],
[{'damning': True}, 'neg'],
[{'damper': True}, 'neg'],
[{'danger': True}, 'neg'],
[{'dangerous': True}, 'neg'],
[{'dangerousness': True}, 'neg'],
[{'dark': True}, 'neg'],
[{'darken': True}, 'neg'],
[{'darkened': True}, 'neg'],
[{'darker': True}, 'neg'],
[{'darkness': True}, 'neg'],
[{'dastard': True}, 'neg'],
[{'dastardly': True}, 'neg'],
[{'daunt': True}, 'neg'],
[{'daunting': True}, 'neg'],
[{'dauntingly': True}, 'neg'],
[{'dawdle': True}, 'neg'],
[{'daze': True}, 'neg'],
[{'dazed': True}, 'neg'],
[{'dead': True}, 'neg'],
[{'deadbeat': True}, 'neg'],
[{'deadlock': True}, 'neg'],
[{'deadly': True}, 'neg'],
[{'deadweight': True}, 'neg'],
[{'deaf': True}, 'neg'],
[{'dearth': True}, 'neg'],
[{'death': True}, 'neg'],
[{'debacle': True}, 'neg'],
[{'debase': True}, 'neg'],
[{'debasement': True}, 'neg'],
[{'debaser': True}, 'neg'],
[{'debatable': True}, 'neg'],
[{'debauch': True}, 'neg'],
[{'debaucher': True}, 'neg'],
[{'debauchery': True}, 'neg'],
[{'debilitate': True}, 'neg'],
[{'debilitating': True}, 'neg'],
[{'debility': True}, 'neg'],
[{'debt': True}, 'neg'],
[{'debts': True}, 'neg'],
[{'decadence': True}, 'neg'],
[{'decadent': True}, 'neg'],
[{'decay': True}, 'neg'],
[{'decayed': True}, 'neg'],
[{'deceit': True}, 'neg'],
[{'deceitful': True}, 'neg'],
[{'deceitfully': True}, 'neg'],
[{'deceitfulness': True}, 'neg'],
[{'deceive': True}, 'neg'],
[{'deceiver': True}, 'neg'],
[{'deceivers': True}, 'neg'],
[{'deceiving': True}, 'neg'],
[{'deception': True}, 'neg'],
[{'deceptive': True}, 'neg'],
[{'deceptively': True}, 'neg'],
[{'declaim': True}, 'neg'],
[{'decline': True}, 'neg'],
[{'declines': True}, 'neg'],
[{'declining': True}, 'neg'],
[{'decrement': True}, 'neg'],
[{'decrepit': True}, 'neg'],
[{'decrepitude': True}, 'neg'],
[{'decry': True}, 'neg'],
[{'defamation': True}, 'neg'],
[{'defamations': True}, 'neg'],
[{'defamatory': True}, 'neg'],
[{'defame': True}, 'neg'],
[{'defect': True}, 'neg'],
[{'defective': True}, 'neg'],
[{'defects': True}, 'neg'],
[{'defensive': True}, 'neg'],
[{'defiance': True}, 'neg'],
[{'defiant': True}, 'neg'],
[{'defiantly': True}, 'neg'],
[{'deficiencies': True}, 'neg'],
[{'deficiency': True}, 'neg'],
[{'deficient': True}, 'neg'],
[{'defile': True}, 'neg'],
[{'defiler': True}, 'neg'],
[{'deform': True}, 'neg'],
[{'deformed': True}, 'neg'],
[{'defrauding': True}, 'neg'],
[{'defunct': True}, 'neg'],
[{'defy': True}, 'neg'],
[{'degenerate': True}, 'neg'],
[{'degenerately': True}, 'neg'],
[{'degeneration': True}, 'neg'],
[{'degradation': True}, 'neg'],
[{'degrade': True}, 'neg'],
[{'degrading': True}, 'neg'],
[{'degradingly': True}, 'neg'],
[{'dehumanization': True}, 'neg'],
[{'dehumanize': True}, 'neg'],
[{'deign': True}, 'neg'],
[{'deject': True}, 'neg'],
[{'dejected': True}, 'neg'],
[{'dejectedly': True}, 'neg'],
[{'dejection': True}, 'neg'],
[{'delay': True}, 'neg'],
[{'delayed': True}, 'neg'],
[{'delaying': True}, 'neg'],
[{'delays': True}, 'neg'],
[{'delinquency': True}, 'neg'],
[{'delinquent': True}, 'neg'],
[{'delirious': True}, 'neg'],
[{'delirium': True}, 'neg'],
[{'delude': True}, 'neg'],
[{'deluded': True}, 'neg'],
[{'deluge': True}, 'neg'],
[{'delusion': True}, 'neg'],
[{'delusional': True}, 'neg'],
[{'delusions': True}, 'neg'],
[{'demean': True}, 'neg'],
[{'demeaning': True}, 'neg'],
[{'demise': True}, 'neg'],
[{'demolish': True}, 'neg'],
[{'demolisher': True}, 'neg'],
[{'demon': True}, 'neg'],
[{'demonic': True}, 'neg'],
[{'demonize': True}, 'neg'],
[{'demonized': True}, 'neg'],
[{'demonizes': True}, 'neg'],
[{'demonizing': True}, 'neg'],
[{'demoralize': True}, 'neg'],
[{'demoralizing': True}, 'neg'],
[{'demoralizingly': True}, 'neg'],
[{'denial': True}, 'neg'],
[{'denied': True}, 'neg'],
[{'denies': True}, 'neg'],
[{'denigrate': True}, 'neg'],
[{'denounce': True}, 'neg'],
[{'dense': True}, 'neg'],
[{'dent': True}, 'neg'],
[{'dented': True}, 'neg'],
[{'dents': True}, 'neg'],
[{'denunciate': True}, 'neg'],
[{'denunciation': True}, 'neg'],
[{'denunciations': True}, 'neg'],
[{'deny': True}, 'neg'],
[{'denying': True}, 'neg'],
[{'deplete': True}, 'neg'],
[{'deplorable': True}, 'neg'],
[{'deplorably': True}, 'neg'],
[{'deplore': True}, 'neg'],
[{'deploring': True}, 'neg'],
[{'deploringly': True}, 'neg'],
[{'deprave': True}, 'neg'],
[{'depraved': True}, 'neg'],
[{'depravedly': True}, 'neg'],
[{'deprecate': True}, 'neg'],
[{'depress': True}, 'neg'],
[{'depressed': True}, 'neg'],
[{'depressing': True}, 'neg'],
[{'depressingly': True}, 'neg'],
[{'depression': True}, 'neg'],
[{'depressions': True}, 'neg'],
[{'deprive': True}, 'neg'],
[{'deprived': True}, 'neg'],
[{'deride': True}, 'neg'],
[{'derision': True}, 'neg'],
[{'derisive': True}, 'neg'],
[{'derisively': True}, 'neg'],
[{'derisiveness': True}, 'neg'],
[{'derogatory': True}, 'neg'],
[{'desecrate': True}, 'neg'],
[{'desert': True}, 'neg'],
[{'desertion': True}, 'neg'],
[{'desiccate': True}, 'neg'],
[{'desiccated': True}, 'neg'],
[{'desititute': True}, 'neg'],
[{'desolate': True}, 'neg'],
[{'desolately': True}, 'neg'],
[{'desolation': True}, 'neg'],
[{'despair': True}, 'neg'],
[{'despairing': True}, 'neg'],
[{'despairingly': True}, 'neg'],
[{'desperate': True}, 'neg'],
[{'desperately': True}, 'neg'],
[{'desperation': True}, 'neg'],
[{'despicable': True}, 'neg'],
[{'despicably': True}, 'neg'],
...]
In [60]:
len(pos_features),len(neg_features)
Out[60]:
(8020, 4783)
In [61]:
trainFeatures = pos_features + neg_features
In [62]:
trainFeatures
Out[62]:
[[{'a+': True}, 'pos'],
[{'abound': True}, 'pos'],
[{'abounds': True}, 'pos'],
[{'abundance': True}, 'pos'],
[{'abundant': True}, 'pos'],
[{'accessable': True}, 'pos'],
[{'accessible': True}, 'pos'],
[{'acclaim': True}, 'pos'],
[{'acclaimed': True}, 'pos'],
[{'acclamation': True}, 'pos'],
[{'accolade': True}, 'pos'],
[{'accolades': True}, 'pos'],
[{'accommodative': True}, 'pos'],
[{'accomodative': True}, 'pos'],
[{'accomplish': True}, 'pos'],
[{'accomplished': True}, 'pos'],
[{'accomplishment': True}, 'pos'],
[{'accomplishments': True}, 'pos'],
[{'accurate': True}, 'pos'],
[{'accurately': True}, 'pos'],
[{'achievable': True}, 'pos'],
[{'achievement': True}, 'pos'],
[{'achievements': True}, 'pos'],
[{'achievible': True}, 'pos'],
[{'acumen': True}, 'pos'],
[{'adaptable': True}, 'pos'],
[{'adaptive': True}, 'pos'],
[{'adequate': True}, 'pos'],
[{'adjustable': True}, 'pos'],
[{'admirable': True}, 'pos'],
[{'admirably': True}, 'pos'],
[{'admiration': True}, 'pos'],
[{'admire': True}, 'pos'],
[{'admirer': True}, 'pos'],
[{'admiring': True}, 'pos'],
[{'admiringly': True}, 'pos'],
[{'adorable': True}, 'pos'],
[{'adore': True}, 'pos'],
[{'adored': True}, 'pos'],
[{'adorer': True}, 'pos'],
[{'adoring': True}, 'pos'],
[{'adoringly': True}, 'pos'],
[{'adroit': True}, 'pos'],
[{'adroitly': True}, 'pos'],
[{'adulate': True}, 'pos'],
[{'adulation': True}, 'pos'],
[{'adulatory': True}, 'pos'],
[{'advanced': True}, 'pos'],
[{'advantage': True}, 'pos'],
[{'advantageous': True}, 'pos'],
[{'advantageously': True}, 'pos'],
[{'advantages': True}, 'pos'],
[{'adventuresome': True}, 'pos'],
[{'adventurous': True}, 'pos'],
[{'advocate': True}, 'pos'],
[{'advocated': True}, 'pos'],
[{'advocates': True}, 'pos'],
[{'affability': True}, 'pos'],
[{'affable': True}, 'pos'],
[{'affably': True}, 'pos'],
[{'affectation': True}, 'pos'],
[{'affection': True}, 'pos'],
[{'affectionate': True}, 'pos'],
[{'affinity': True}, 'pos'],
[{'affirm': True}, 'pos'],
[{'affirmation': True}, 'pos'],
[{'affirmative': True}, 'pos'],
[{'affluence': True}, 'pos'],
[{'affluent': True}, 'pos'],
[{'afford': True}, 'pos'],
[{'affordable': True}, 'pos'],
[{'affordably': True}, 'pos'],
[{'afordable': True}, 'pos'],
[{'agile': True}, 'pos'],
[{'agilely': True}, 'pos'],
[{'agility': True}, 'pos'],
[{'agreeable': True}, 'pos'],
[{'agreeableness': True}, 'pos'],
[{'agreeably': True}, 'pos'],
[{'all-around': True}, 'pos'],
[{'alluring': True}, 'pos'],
[{'alluringly': True}, 'pos'],
[{'altruistic': True}, 'pos'],
[{'altruistically': True}, 'pos'],
[{'amaze': True}, 'pos'],
[{'amazed': True}, 'pos'],
[{'amazement': True}, 'pos'],
[{'amazes': True}, 'pos'],
[{'amazing': True}, 'pos'],
[{'amazingly': True}, 'pos'],
[{'ambitious': True}, 'pos'],
[{'ambitiously': True}, 'pos'],
[{'ameliorate': True}, 'pos'],
[{'amenable': True}, 'pos'],
[{'amenity': True}, 'pos'],
[{'amiability': True}, 'pos'],
[{'amiabily': True}, 'pos'],
[{'amiable': True}, 'pos'],
[{'amicability': True}, 'pos'],
[{'amicable': True}, 'pos'],
[{'amicably': True}, 'pos'],
[{'amity': True}, 'pos'],
[{'ample': True}, 'pos'],
[{'amply': True}, 'pos'],
[{'amuse': True}, 'pos'],
[{'amusing': True}, 'pos'],
[{'amusingly': True}, 'pos'],
[{'angel': True}, 'pos'],
[{'angelic': True}, 'pos'],
[{'apotheosis': True}, 'pos'],
[{'appeal': True}, 'pos'],
[{'appealing': True}, 'pos'],
[{'applaud': True}, 'pos'],
[{'appreciable': True}, 'pos'],
[{'appreciate': True}, 'pos'],
[{'appreciated': True}, 'pos'],
[{'appreciates': True}, 'pos'],
[{'appreciative': True}, 'pos'],
[{'appreciatively': True}, 'pos'],
[{'appropriate': True}, 'pos'],
[{'approval': True}, 'pos'],
[{'approve': True}, 'pos'],
[{'ardent': True}, 'pos'],
[{'ardently': True}, 'pos'],
[{'ardor': True}, 'pos'],
[{'articulate': True}, 'pos'],
[{'aspiration': True}, 'pos'],
[{'aspirations': True}, 'pos'],
[{'aspire': True}, 'pos'],
[{'assurance': True}, 'pos'],
[{'assurances': True}, 'pos'],
[{'assure': True}, 'pos'],
[{'assuredly': True}, 'pos'],
[{'assuring': True}, 'pos'],
[{'astonish': True}, 'pos'],
[{'astonished': True}, 'pos'],
[{'astonishing': True}, 'pos'],
[{'astonishingly': True}, 'pos'],
[{'astonishment': True}, 'pos'],
[{'astound': True}, 'pos'],
[{'astounded': True}, 'pos'],
[{'astounding': True}, 'pos'],
[{'astoundingly': True}, 'pos'],
[{'astutely': True}, 'pos'],
[{'attentive': True}, 'pos'],
[{'attraction': True}, 'pos'],
[{'attractive': True}, 'pos'],
[{'attractively': True}, 'pos'],
[{'attune': True}, 'pos'],
[{'audible': True}, 'pos'],
[{'audibly': True}, 'pos'],
[{'auspicious': True}, 'pos'],
[{'authentic': True}, 'pos'],
[{'authoritative': True}, 'pos'],
[{'autonomous': True}, 'pos'],
[{'available': True}, 'pos'],
[{'aver': True}, 'pos'],
[{'avid': True}, 'pos'],
[{'avidly': True}, 'pos'],
[{'award': True}, 'pos'],
[{'awarded': True}, 'pos'],
[{'awards': True}, 'pos'],
[{'awe': True}, 'pos'],
[{'awed': True}, 'pos'],
[{'awesome': True}, 'pos'],
[{'awesomely': True}, 'pos'],
[{'awesomeness': True}, 'pos'],
[{'awestruck': True}, 'pos'],
[{'awsome': True}, 'pos'],
[{'backbone': True}, 'pos'],
[{'balanced': True}, 'pos'],
[{'bargain': True}, 'pos'],
[{'beauteous': True}, 'pos'],
[{'beautiful': True}, 'pos'],
[{'beautifullly': True}, 'pos'],
[{'beautifully': True}, 'pos'],
[{'beautify': True}, 'pos'],
[{'beauty': True}, 'pos'],
[{'beckon': True}, 'pos'],
[{'beckoned': True}, 'pos'],
[{'beckoning': True}, 'pos'],
[{'beckons': True}, 'pos'],
[{'believable': True}, 'pos'],
[{'believeable': True}, 'pos'],
[{'beloved': True}, 'pos'],
[{'benefactor': True}, 'pos'],
[{'beneficent': True}, 'pos'],
[{'beneficial': True}, 'pos'],
[{'beneficially': True}, 'pos'],
[{'beneficiary': True}, 'pos'],
[{'benefit': True}, 'pos'],
[{'benefits': True}, 'pos'],
[{'benevolence': True}, 'pos'],
[{'benevolent': True}, 'pos'],
[{'benifits': True}, 'pos'],
[{'best': True}, 'pos'],
[{'best-known': True}, 'pos'],
[{'best-performing': True}, 'pos'],
[{'best-selling': True}, 'pos'],
[{'better': True}, 'pos'],
[{'better-known': True}, 'pos'],
[{'better-than-expected': True}, 'pos'],
[{'beutifully': True}, 'pos'],
[{'blameless': True}, 'pos'],
[{'bless': True}, 'pos'],
[{'blessing': True}, 'pos'],
[{'bliss': True}, 'pos'],
[{'blissful': True}, 'pos'],
[{'blissfully': True}, 'pos'],
[{'blithe': True}, 'pos'],
[{'blockbuster': True}, 'pos'],
[{'bloom': True}, 'pos'],
[{'blossom': True}, 'pos'],
[{'bolster': True}, 'pos'],
[{'bonny': True}, 'pos'],
[{'bonus': True}, 'pos'],
[{'bonuses': True}, 'pos'],
[{'boom': True}, 'pos'],
[{'booming': True}, 'pos'],
[{'boost': True}, 'pos'],
[{'boundless': True}, 'pos'],
[{'bountiful': True}, 'pos'],
[{'brainiest': True}, 'pos'],
[{'brainy': True}, 'pos'],
[{'brand-new': True}, 'pos'],
[{'brave': True}, 'pos'],
[{'bravery': True}, 'pos'],
[{'bravo': True}, 'pos'],
[{'breakthrough': True}, 'pos'],
[{'breakthroughs': True}, 'pos'],
[{'breathlessness': True}, 'pos'],
[{'breathtaking': True}, 'pos'],
[{'breathtakingly': True}, 'pos'],
[{'breeze': True}, 'pos'],
[{'bright': True}, 'pos'],
[{'brighten': True}, 'pos'],
[{'brighter': True}, 'pos'],
[{'brightest': True}, 'pos'],
[{'brilliance': True}, 'pos'],
[{'brilliances': True}, 'pos'],
[{'brilliant': True}, 'pos'],
[{'brilliantly': True}, 'pos'],
[{'brisk': True}, 'pos'],
[{'brotherly': True}, 'pos'],
[{'bullish': True}, 'pos'],
[{'buoyant': True}, 'pos'],
[{'cajole': True}, 'pos'],
[{'calm': True}, 'pos'],
[{'calming': True}, 'pos'],
[{'calmness': True}, 'pos'],
[{'capability': True}, 'pos'],
[{'capable': True}, 'pos'],
[{'capably': True}, 'pos'],
[{'captivate': True}, 'pos'],
[{'captivating': True}, 'pos'],
[{'carefree': True}, 'pos'],
[{'cashback': True}, 'pos'],
[{'cashbacks': True}, 'pos'],
[{'catchy': True}, 'pos'],
[{'celebrate': True}, 'pos'],
[{'celebrated': True}, 'pos'],
[{'celebration': True}, 'pos'],
[{'celebratory': True}, 'pos'],
[{'champ': True}, 'pos'],
[{'champion': True}, 'pos'],
[{'charisma': True}, 'pos'],
[{'charismatic': True}, 'pos'],
[{'charitable': True}, 'pos'],
[{'charm': True}, 'pos'],
[{'charming': True}, 'pos'],
[{'charmingly': True}, 'pos'],
[{'chaste': True}, 'pos'],
[{'cheaper': True}, 'pos'],
[{'cheapest': True}, 'pos'],
[{'cheer': True}, 'pos'],
[{'cheerful': True}, 'pos'],
[{'cheery': True}, 'pos'],
[{'cherish': True}, 'pos'],
[{'cherished': True}, 'pos'],
[{'cherub': True}, 'pos'],
[{'chic': True}, 'pos'],
[{'chivalrous': True}, 'pos'],
[{'chivalry': True}, 'pos'],
[{'civility': True}, 'pos'],
[{'civilize': True}, 'pos'],
[{'clarity': True}, 'pos'],
[{'classic': True}, 'pos'],
[{'classy': True}, 'pos'],
[{'clean': True}, 'pos'],
[{'cleaner': True}, 'pos'],
[{'cleanest': True}, 'pos'],
[{'cleanliness': True}, 'pos'],
[{'cleanly': True}, 'pos'],
[{'clear': True}, 'pos'],
[{'clear-cut': True}, 'pos'],
[{'cleared': True}, 'pos'],
[{'clearer': True}, 'pos'],
[{'clearly': True}, 'pos'],
[{'clears': True}, 'pos'],
[{'clever': True}, 'pos'],
[{'cleverly': True}, 'pos'],
[{'cohere': True}, 'pos'],
[{'coherence': True}, 'pos'],
[{'coherent': True}, 'pos'],
[{'cohesive': True}, 'pos'],
[{'colorful': True}, 'pos'],
[{'comely': True}, 'pos'],
[{'comfort': True}, 'pos'],
[{'comfortable': True}, 'pos'],
[{'comfortably': True}, 'pos'],
[{'comforting': True}, 'pos'],
[{'comfy': True}, 'pos'],
[{'commend': True}, 'pos'],
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[{'impressively': True}, 'pos'],
[{'impressiveness': True}, 'pos'],
[{'improve': True}, 'pos'],
[{'improved': True}, 'pos'],
[{'improvement': True}, 'pos'],
[{'improvements': True}, 'pos'],
[{'improves': True}, 'pos'],
[{'improving': True}, 'pos'],
[{'incredible': True}, 'pos'],
[{'incredibly': True}, 'pos'],
[{'indebted': True}, 'pos'],
[{'individualized': True}, 'pos'],
[{'indulgence': True}, 'pos'],
[{'indulgent': True}, 'pos'],
[{'industrious': True}, 'pos'],
[{'inestimable': True}, 'pos'],
[{'inestimably': True}, 'pos'],
[{'inexpensive': True}, 'pos'],
[{'infallibility': True}, 'pos'],
[{'infallible': True}, 'pos'],
[{'infallibly': True}, 'pos'],
[{'influential': True}, 'pos'],
[{'ingenious': True}, 'pos'],
[{'ingeniously': True}, 'pos'],
[{'ingenuity': True}, 'pos'],
[{'ingenuous': True}, 'pos'],
[{'ingenuously': True}, 'pos'],
[{'innocuous': True}, 'pos'],
[{'innovation': True}, 'pos'],
[{'innovative': True}, 'pos'],
[{'inpressed': True}, 'pos'],
[{'insightful': True}, 'pos'],
...]
In [63]:
classifier = NaiveBayesClassifier.train(trainFeatures)
In [64]:
referenceSets = collections.defaultdict(set)
testSets = collections.defaultdict(set)
In [65]:
def make_full_dict_sent(words):
return dict([(word, True) for word in words])
In [66]:
import re
In [67]:
neg_test = 'I hate data science'
In [68]:
title_words = re.findall(r"[\w']+|[.,!?;]",
'The Daily Mail stole My Visualization, Twice')
In [69]:
title_words
Out[69]:
['The', 'Daily', 'Mail', 'stole', 'My', 'Visualization', ',', 'Twice']
In [70]:
test=[]
In [71]:
test.append([make_full_dict_sent(title_words),''])
In [72]:
test
Out[72]:
[[{',': True,
'Daily': True,
'Mail': True,
'My': True,
'The': True,
'Twice': True,
'Visualization': True,
'stole': True},
'']]
In [73]:
for i, (features, label) in enumerate(test):
predicted = classifier.classify(features)
print(predicted)
neg
In [74]:
for doc in df.title:
title_words = re.findall(r"[\w']+|[.,!?;]", doc.lower())
test = []
test.append([make_full_dict_sent(title_words),''])
for i, (features, label) in enumerate(test):
predicted = classifier.classify(features)
print(predicted,doc)
pos Deep Advances in Generative Modeling
pos A Neural Network in 11 lines of Python
pos Python, Machine Learning, and Language Wars
pos Markov Chains Explained Visually
pos Dplython: Dplyr for Python
pos Inferring causal impact using Bayesian structural time-series models
pos Tutorial: Web scraping and mapping breweries with import.io and R
pos A Billion Taxi Rides on Amazon EMR running Spark
neg The rise of greedy robots
pos Extracting image metadata at scale
pos Python for Data Structures, Algorithms, and Interviews
pos Lift charts - A data scientist's secret weapon
pos How To Become A Machine Learning Expert In One Simple Step
pos Data Science Side Project
pos Simple estimation of hierarchical events with petersburg
pos Engineers Shouldn?t Write ETL: High Functioning Data Science Departments
pos Unsupervised Computer Vision: The Current State of the Art
pos What data visualization tools do /r/DataIsBeautiful OC creators use?
neg Data Engineering at Slack: Twelve Mistakes I've Made In My First Three Months
neg An unusual interactive machine learning challenge
pos Datumbox Machine Learning Framework 0.7.0 Released
pos Reshaping in Pandas
pos Data science intro for math/phys background
pos Neural Networks demystified
pos What machines can learn from Apple Watch: detecting undiagnosed heart condition
pos Data Science Tools: The Biggest Winners and Losers
pos 10 Years of Open Source Machine Learning
pos Do jobs run in families?
pos Has your conversion rate changed? Bayesian timeseries analysis with Python
pos XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow
pos Introduction to Scikit Flow - Simplified Interface to TensorFlow
pos How to learn machine learning?
neg The Deep Roots of Javascript Fatigue
pos How do we make Data Tau work?
pos Machine Learning: An In-Depth, Non-Technical Guide???Part 4
neg Data Science Slack channel - Click for invite
pos Genomic Data Visualization using Python
pos Descriptive Statistics in SQL
pos Playing "Moneyball" on EA FIFA 16
pos Intellexer - Natural Language Processing and Text Mining REST API
pos How to Use Cohort Data to Analyze User Behavior
pos Show DT: Datasets.co - An easy way to share and discover ml datasets
pos An Ode To The Rice Cooker, The Smartest Kitchen Appliance I?ve Ever Owned
pos Making transparent how variations in analytical choices affect results
neg [Ask DT] What are some rookie mistakes in R?
pos Is Scala a better choice than Python for Apache Spark?
pos Julia: A Fast Language for Numerical Computing
pos Analyzing Golden State Warriors' passing network using GraphFrames in Spark
pos Megaman: Manifold Learning with Millions of points
pos How to Detect Outliers on Parametric and Non Parametric Methods
pos BallR: Interactive NBA Shot Charts with R and Shiny
pos Minecraft to run artificial intelligence experiments
pos Deep Q-Learning (Space Invaders)
pos Theano Tutorial
pos Computing Classification Evaluation Metrics in R
pos The Personality Space of Cartoon Characters
pos Announcing Apache Flink 1.0.0
pos Bayesian Reasoning in The Twilight Zone!
pos Bayesian Estimation of G Train Wait Times
neg Some experiments into explaining complex black box ensemble predictions
pos Creating a Hadoop Pseudo-Distributed Environment
pos Data Science Pop-Up in Austin, TX
pos A Billion Taxi Rides on Amazon EMR Running Presto
pos Train your own image classifier with Inception in TensorFlow
pos Statisticians Agree: It?s Time To Stop Misusing P-Value
pos Shiny app for running a Tensorflow demo
pos File details and owners with gitnoc and git-pandas
pos 7 Big Data Technologies and When to Use Them that All Data Engineers Should Know
pos Topic clusters with TF-IDF vectorization with Spark and Scala
pos Neural Doodles: Workflows for the Next Generation of Artists
pos Graph Databases 101
pos Telemetry with Collectd, Logstash, Elasticsearch and Grafana (ELG)
pos XGBoost: A Scalable Tree Boosting System article
pos DataRadar.IO - Data Science RSS Feed - Do you have enough data about your data
pos International Women's Day: What #PledgeForParity Means To Us
pos Top 50 Data Science thought leaders on Twitter
pos Ask DT: Who Is Hiring? (March 2016)
pos Introducing GraphFrames
pos Announcing R Tools for Visual Studio
pos Question: What do you want to say about working with data?
pos Genomic Ranges - an Introduction to Working with Genomic Data
pos TensorFlow for Poets
pos Unsupervised Learning with Even Less Supervision Using Bayesian Optimization
pos How to work with large JSON datasets using Python and Pandas
pos DrivenData Competition: Model/Visualize Fog Patterns in Morocco
pos Deriving Better Insights From Time Series Data With Cycle Plots
pos Deep Learning: Nine Lectures at Coll?ge de France by Yan LeCun
pos SQL for Data Analysis
pos Stream processing and messaging systems for the IoT age
pos Optimizing Facebook Campaigns with R
pos Trump Tweets on a Globe (aka Fun with d3, socket.io, and the Twitter API)
pos Why pandas users should be excited about Apache Arrow
pos Histogram intersection for change detection
pos A simpler way to merge data streams
pos Distributed TensorFlow just open-sourced
pos D3.js Screencasts (1 in 3 are free)
neg Regression and Classification with Examples in R
pos Free online course on statistical shape modelling
neg Don't worry about deep learning, deepen your understanding of causality instead
pos Skizze - A high throughput probabilistic data structure service and storage
pos Work with private repositories and other updates of the FlyElephant platform
pos How to import XML to almost anywhere
pos Optimizing Notification Timing for One Signal
pos Survival Analysis of Cricket Player Careers
pos Generate image analogies using neural matching and blending
pos Analyzing 1.8M tweets from Super Bowl 50 (Twython, Twitter API, AYLIEN)
pos Newly released sklearn compatible library of categorical encoders
pos Watch Tiny Neural Nets Learn
pos Four pitfalls of hill climbing: An animated look
pos Decision Forests, Convolutional Networks and the Models in-Between
pos How a Math Genius Hacked OkCupid to Find True Love
pos No developers for PyLearn2
pos Density Estimation with Dirichlet Process Mixtures using PyMC3
pos Using survival analysis and git-pandas to estimate code quality
neg An Analysis of the Flint Michigan Water Crisis: Part 1 Initial Corrosivity
pos An Analysis of Republican Twitter Follower Interests
pos Introduction to ML talk
pos GloVe vs word2vec revisited
pos Undergrad Data Analysis/Science internships SF Bay?
pos The Role of Statistical Significance in Growth Hacking
pos Data Science Course @ Harvard
pos Principal Component Projection Without Principal Component Analysis
pos Machine Learning: An In-Depth, Non-Technical Guide - Part 3
pos Stochastic Dummy Boosting
pos Interactive Map: Hong-Kong through The Lense of Instagram
pos Data Science at Monsanto
pos Data Science at Instacart
pos Building a Streaming Search Platform
neg A Sneak Peak of the Cloud: the 2 Minute Intro for Beginners
pos Win-Vector video courses: price/status changes
neg 50+ Data Science and Machine Learning Cheat Sheets
neg One More Reason Not To Be Scared of Deep Learning
pos Visual Logic Authoring vs Code
pos Data Science in Python online training with hands-on experience
pos Viewing the US Presidential Primary Through the Lens of Twitter
pos Caffe on Spark open sourced
pos The Ethical Data Scientist
pos Answers to Frequently Asked Questions in Machine Learning
pos Intro to A/B Testing and P-Values
pos Visualizing State Level Data With R and Statebins
pos Probabilistic Graphical Models slides & video lectures (Eric Xing, CMU)
pos Sense2vec with spaCy and Gensim
pos How to Code and Understand DeepMind's Neural Stack Machine (in Python)
pos How to make polished Jupyter presentations with optional code visibility
pos How to become a Bayesian in eight easy steps
pos Optimizing .*: Details of Vectorization and Metaprogramming in Julia
pos IBM certified Apache Spark Online Training
pos Geographic Data Science course
neg The Daily Mail Stole My Visualization, Twice
pos Ensemble Methods: Improved Machine Learning Results
pos Apache Spark and unsupervised learning in security
pos MachineJS: Automated machine learning- just give it a data file!
neg Kafka Producer Latency with Large Topic Counts
neg The NSA?s SKYNET program may be killing thousands of innocent people
pos Overoptimizing: a story about kaggle
pos Big Dimensions, and What You Can Do About It
pos Automate Your Oscars Pool with R
pos Signal Processing with LIGO GW150914 data
pos Overview of DeZyre and Coursera Data Science Course
pos Upcoming Datathon in NYC
pos Summarizing Data in SQL
pos A/B Testing for Scammers
pos Highly interpretable classifiers for scikit learn using Bayesian decision rules
pos Auto-scaling scikit-learn with Spark
pos Where the f*** can I park?
pos Machine Learning: An In-Depth, Non-Technical Guide - Part 2
pos Webhose.io now offers a historical data archive
pos Meetup: Introduction to Machine Learning Algorithms for Data Science.
neg Exploring the Limits of Language Modeling
pos Text Mining South Park
pos Finding the K in K-means by Parametric Bootstrap
pos A Billion NYC Taxi and Uber Rides in AWS Redshift
pos Getting Started with Statistics for Data Science
pos Rodeo 1.3 - Tab-completion for docstrings
pos Teaching D3.js - links
pos Parallel scikit-learn on YARN
pos Meetup: Free Live Webinar on Prescriptive Analytics for Fun and Profit
pos Access to VK.com (Vkontakte) API via R
pos Deep Learning Tutorial by Y. LeCun and Y. Bengio
pos Machine Learning Meets Economics
Content source: amitkaps/machine-learning
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