In [153]:

    

import os
os.chdir('/Users/sheldon/git/podly_app/new_files')


    

In [154]:

    

import glob
files = [f for f in os.listdir('.') if os.path.isfile(f)]
files = glob.glob('*.txt')


    

In [155]:

    

import pandas as pd
series = []
for i in files:
    series.append(i.split('.mp3')[0])


    

In [214]:

    

x = pd.DataFrame(series)
def read_podcast(file):
    tempFile = open(file).read()
    return tempFile


    

In [215]:

    

transcriptions = []
for i in files:
    transcription = open(i).read()
    transcriptions.append(transcription)


    

In [216]:

    

x['transcription'] = transcriptions


    

In [217]:

    

x.head()


    

    
Out[217]:






  

    

      

      
0
      
transcription
    
  
  

    

      
0
      
askmeanother-cameronesposito
      
support for the spot cast comes from being fun...
    
    

      
1
      
bullseye-olson-and-chang
      
and useful silent jesse whining you didn't get...
    
    

      
2
      
fivethirtyeightelections_2016-10-05-140700-800...
      
i'm having lived in the latest news of the sta...
    
    

      
3
      
freakonomics_podcast092816
      
freedom it's radio is supported by a concurren...
    
    

      
4
      
joerogan-p856
      
they were going to post on this episode of con...
    
  

In [218]:

    

x.columns = ['file','transcription']
fileList = range(1, len(files)+1)
x['index'] = fileList
x.head()


    

    
Out[218]:






  

    

      

      
file
      
transcription
      
index
    
  
  

    

      
0
      
askmeanother-cameronesposito
      
support for the spot cast comes from being fun...
      
1
    
    

      
1
      
bullseye-olson-and-chang
      
and useful silent jesse whining you didn't get...
      
2
    
    

      
2
      
fivethirtyeightelections_2016-10-05-140700-800...
      
i'm having lived in the latest news of the sta...
      
3
    
    

      
3
      
freakonomics_podcast092816
      
freedom it's radio is supported by a concurren...
      
4
    
    

      
4
      
joerogan-p856
      
they were going to post on this episode of con...
      
5
    
  

In [219]:

    

from nltk.corpus import stopwords
stop = set(stopwords.words('english'))


    

In [73]:

    

def tokenize_and_lower(textfile):
    tokens = word_tokenize(textfile)
    lower = [w.lower() for w in tokens]
    filtered_words = [word for word in lower if word not in stop]
    return filtered_words

from nltk.corpus import stopwords
from collections import Counter
import pandas as pd
import numpy as np
import nltk.data
from __future__ import division  # Python 2 users only
import nltk, re, pprint
from nltk import word_tokenize


    

In [213]:

    

tokenized_all_docs = [tokenize_and_lower(doc) for doc in transcriptions]
tokenized_all_docs


    

    
Out[213]:





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  'presenting',
  'comedy',
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  'fully',
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  'comedy',
  'festival',
  'happening',
  'cobra',
  'twenty',
  'ninth',
  'listeners',
  'good',
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  'percent',
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  'comedy',
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  'coax',
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  'bill',
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  'comedienne',
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  'words',
  'oh',
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  'sure',
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  'first',
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  'wat',
  ...]]

In [75]:

    

from gensim import corpora


    

In [76]:

    

dictionary = corpora.Dictionary(tokenized_all_docs)


    

In [77]:

    

dictionary.save('sample.dict')


    

In [188]:

    

print dictionary


    

    




Dictionary(6806 unique tokens: [u'four', u'woods', u'verses', u'railing', u'woody']...)

In [191]:

    

corpus = [dictionary.doc2bow(text) for text in tokenized_all_docs]
corpora.MmCorpus.serialize('corpus.mm', corpus)


    

In [84]:

    

from gensim import models, corpora, similarities


    

In [207]:

    

tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=10)
corpus_lsi = lsi[corpus_tfidf]
index = similarities.MatrixSimilarity(lsi[corpus])


    

In [208]:

    

lsi.print_topics(10)


    

    
Out[208]:





[(0,
  u'0.135*"cash" + 0.112*"fire" + 0.098*"american" + 0.096*"morgan" + 0.086*"somalia" + 0.076*"correct" + 0.073*"shit" + 0.072*"embassy" + 0.072*"kenya" + 0.070*"winner"'),
 (1,
  u'-0.233*"democratic" + -0.233*"election" + 0.224*"fire" + -0.192*"muslim" + -0.133*"clinton" + -0.131*"immigrants" + -0.129*"americans" + -0.128*"voters" + -0.118*"industry" + -0.117*"demographics"'),
 (2,
  u'0.398*"cash" + 0.147*"sweden" + 0.125*"fire" + 0.122*"bills" + 0.121*"banks" + -0.103*"corley" + -0.103*"totems" + -0.103*"lectures" + 0.094*"billion" + -0.089*"courses"'),
 (3,
  u'-0.158*"fire" + 0.149*"morgan" + -0.131*"democratic" + -0.131*"election" + 0.119*"correct" + 0.109*"winner" + -0.107*"muslim" + 0.102*"performance" + -0.092*"voters" + 0.085*"character"'),
 (4,
  u'-0.177*"cash" + 0.173*"connection" + 0.151*"significant" + -0.113*"corley" + -0.113*"totems" + -0.113*"lectures" + 0.108*"patterns" + 0.108*"surprises" + -0.100*"courses" + 0.087*"return"'),
 (5,
  u'-0.276*"cash" + 0.245*"fire" + -0.141*"connection" + 0.136*"apartment" + -0.123*"significant" + 0.121*"burned" + -0.108*"sweden" + 0.091*"cremation" + 0.091*"pile" + -0.088*"patterns"'),
 (6,
  u'-0.240*"cash" + 0.152*"morgan" + 0.122*"correct" + 0.112*"winner" + -0.105*"fire" + -0.101*"performance" + -0.091*"sweden" + 0.086*"shit" + -0.084*"portland" + 0.084*"crate"'),
 (7,
  u'-0.174*"morgan" + -0.139*"correct" + -0.128*"winner" + 0.120*"somalia" + 0.119*"american" + -0.112*"election" + -0.112*"democratic" + 0.101*"embassy" + 0.101*"kenya" + 0.094*"berkeley"'),
 (8,
  u'0.182*"morgan" + -0.159*"crate" + 0.145*"correct" + -0.141*"shit" + 0.133*"winner" + -0.103*"loot" + -0.090*"j" + -0.080*"combo" + -0.080*"subscription" + -0.080*"microphone"'),
 (9,
  u'0.207*"somalia" + 0.176*"embassy" + 0.176*"kenya" + 0.160*"somali" + -0.093*"berkeley" + -0.089*"cultural" + -0.089*"thorn" + -0.089*"broken" + -0.084*"color" + -0.084*"jesse"')]

In [ ]:

    




    

In [209]:

    

def get_related_podcasts_query(query):
    query = query.lower()
    vec_box = dictionary.doc2bow(query.split())
    vec_lsi = lsi[vec_box]
    sims = index[vec_lsi]
    sims = sorted(enumerate(sims), key=lambda item: -item[1])[0:10]
    related_df = pd.DataFrame(sims,columns=['index','score'])
    final_df = pd.merge(related_df, x, on='index')[['index','file','score']]
    return final_df

def get_related_podcasts(index):
    def getKey(item):
        return item[1]
    corpus = corpus_lsi[index]
    corpus = sorted(corpus, key=getKey, reverse=True)[0:10]
    related_df = pd.DataFrame(corpus,columns=['index','score'])
    final_df = pd.merge(related_df, x, on='index')[['index','file','score']]
    return final_df


    

In [221]:

    

get_related_podcasts_query('math')


    

    
Out[221]:






  

    

      

      
index
      
file
      
score
    
  
  

    

      
0
      
5
      
joerogan-p856
      
0.795419
    
    

      
1
      
8
      
THE_BLACK_TAPES_EPISODE_212_-_The_Axis_Mundi
      
0.614362
    
    

      
2
      
1
      
askmeanother-cameronesposito
      
0.084463
    
    

      
3
      
4
      
freakonomics_podcast092816
      
0.074925
    
    

      
4
      
9
      
this-american-life-abdi-560
      
0.064841
    
    

      
5
      
6
      
Lore-44fromwithin
      
0.064163
    
    

      
6
      
7
      
magificent_seven_and_fleabag
      
0.063497
    
    

      
7
      
3
      
fivethirtyeightelections_2016-10-05-140700-800...
      
0.052477
    
    

      
8
      
2
      
bullseye-olson-and-chang
      
0.048643
    
  

In [210]:

    

related_podcasts = list(get_related_podcasts(1)['index'])


    

In [211]:

    

def get_topics_per_podcast(podcast_index):
    def getKey(item):
        return item[1]
    topic_ids = [i for i in sorted(corpus_lsi[podcast_index], key=getKey, reverse=True) if i[1] > 0.10]
    def get_topic_arrays(topic_ids):
        x = []
        for id in topic_ids:
            list_of_words = sorted(lsi.show_topic(id[0], topn=5),key=getKey, reverse=True)
            z = []
            for word in list_of_words:
                if word[1] > .05:
                    z.append(word)
            x.append(z)
        return x
    topic_arrays = get_topic_arrays(topic_ids)
    return topic_arrays
related_podcasts_topics_words = [[related_podcasts[i],get_topics_per_podcast(related_podcasts[i])] for i in range(0, len(related_podcasts))]
episode_podcasts = list(get_related_podcasts(1)['file'])


    

In [212]:

    

for i,k in enumerate(related_podcasts_topics_words):
    print "Podcast: {}, ID: {}".format(i+1, k[0])
    print "Episode Title: {}".format(episode_podcasts[i])
    for num, topic in enumerate(k[1]):
        print "topic: {}".format(num)
        for word in topic:
            print "word: {}, score:{}".format(word[0], word[1])


    

    




Podcast: 1, ID: 7
Episode Title: magificent_seven_and_fleabag
topic: 0
word: cash, score:0.135160354963
word: fire, score:0.112313127804
word: american, score:0.0984519314402
word: morgan, score:0.0955392129237
word: somalia, score:0.085668201488
topic: 1
word: fire, score:0.224434183534
Podcast: 2, ID: 3
Episode Title: fivethirtyeightelections_2016-10-05-140700-8006-0-1-0.64k
topic: 0
word: cash, score:0.397700118953
word: sweden, score:0.147301438019
word: fire, score:0.124866497252
word: bills, score:0.121713914841
word: banks, score:0.121485976494
topic: 1
word: cash, score:0.135160354963
word: fire, score:0.112313127804
word: american, score:0.0984519314402
word: morgan, score:0.0955392129237
word: somalia, score:0.085668201488
topic: 2
word: morgan, score:0.148718513825
word: correct, score:0.11897481106
Podcast: 3, ID: 6
Episode Title: Lore-44fromwithin
topic: 0
word: morgan, score:0.148718513825
word: correct, score:0.11897481106
topic: 1
word: cash, score:0.135160354963
word: fire, score:0.112313127804
word: american, score:0.0984519314402
word: morgan, score:0.0955392129237
word: somalia, score:0.085668201488
topic: 2
word: somalia, score:0.207497436507
word: kenya, score:0.175574753967
word: embassy, score:0.175574753967
word: somali, score:0.159613412697
topic: 3
word: fire, score:0.244741277332
word: apartment, score:0.13589535853
topic: 4
word: connection, score:0.173119560737
word: significant, score:0.151479615644
Podcast: 4, ID: 8
Episode Title: THE_BLACK_TAPES_EPISODE_212_-_The_Axis_Mundi
topic: 0
word: somalia, score:0.207497436507
word: kenya, score:0.175574753967
word: embassy, score:0.175574753967
word: somali, score:0.159613412697
topic: 1
word: cash, score:0.135160354963
word: fire, score:0.112313127804
word: american, score:0.0984519314402
word: morgan, score:0.0955392129237
word: somalia, score:0.085668201488
topic: 2
word: somalia, score:0.119759040946
word: american, score:0.119498349352
topic: 3
word: morgan, score:0.152313822197
word: correct, score:0.121851057757
word: winner, score:0.111696802944
topic: 4
word: morgan, score:0.181786267355
word: correct, score:0.145429013884
word: winner, score:0.133309929393
topic: 5
word: fire, score:0.244741277332
word: apartment, score:0.13589535853
topic: 6
word: cash, score:0.397700118953
word: sweden, score:0.147301438019
word: fire, score:0.124866497252
word: bills, score:0.121713914841
word: banks, score:0.121485976494
Podcast: 5, ID: 5
Episode Title: joerogan-p856
topic: 0
word: fire, score:0.244741277332
word: apartment, score:0.13589535853
topic: 1
word: fire, score:0.224434183534
topic: 2
word: cash, score:0.135160354963
word: fire, score:0.112313127804
word: american, score:0.0984519314402
word: morgan, score:0.0955392129237
word: somalia, score:0.085668201488
topic: 3
word: cash, score:0.397700118953
word: sweden, score:0.147301438019
word: fire, score:0.124866497252
word: bills, score:0.121713914841
word: banks, score:0.121485976494
topic: 4
word: morgan, score:0.181786267355
word: correct, score:0.145429013884
word: winner, score:0.133309929393
topic: 5
word: connection, score:0.173119560737
word: significant, score:0.151479615644
Podcast: 6, ID: 4
Episode Title: freakonomics_podcast092816
topic: 0
word: cash, score:0.135160354963
word: fire, score:0.112313127804
word: american, score:0.0984519314402
word: morgan, score:0.0955392129237
word: somalia, score:0.085668201488
topic: 1
word: morgan, score:0.152313822197
word: correct, score:0.121851057757
word: winner, score:0.111696802944
topic: 2
word: fire, score:0.224434183534
topic: 3
word: cash, score:0.397700118953
word: sweden, score:0.147301438019
word: fire, score:0.124866497252
word: bills, score:0.121713914841
word: banks, score:0.121485976494
Podcast: 7, ID: 1
Episode Title: askmeanother-cameronesposito
topic: 0
word: somalia, score:0.119759040946
word: american, score:0.119498349352
topic: 1
word: cash, score:0.135160354963
word: fire, score:0.112313127804
word: american, score:0.0984519314402
word: morgan, score:0.0955392129237
word: somalia, score:0.085668201488
topic: 2
word: morgan, score:0.148718513825
word: correct, score:0.11897481106
Podcast: 8, ID: 2
Episode Title: bullseye-olson-and-chang
topic: 0
word: cash, score:0.135160354963
word: fire, score:0.112313127804
word: american, score:0.0984519314402
word: morgan, score:0.0955392129237
word: somalia, score:0.085668201488
topic: 1
word: fire, score:0.244741277332
word: apartment, score:0.13589535853
topic: 2
word: connection, score:0.173119560737
word: significant, score:0.151479615644
Podcast: 9, ID: 9
Episode Title: this-american-life-abdi-560
topic: 0
word: connection, score:0.173119560737
word: significant, score:0.151479615644
topic: 1
word: cash, score:0.135160354963
word: fire, score:0.112313127804
word: american, score:0.0984519314402
word: morgan, score:0.0955392129237
word: somalia, score:0.085668201488
topic: 2
word: fire, score:0.224434183534
topic: 3
word: morgan, score:0.181786267355
word: correct, score:0.145429013884
word: winner, score:0.133309929393

In [ ]: