My notebook to learn Word
https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
Import the pandas package, then use the "read_csv" function to read the labeled training data
In [43]:
# Import
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
import nltk
The necessary files can be downloaded from the Data page. The first file that you'll need is unlabeledTrainData.tsv, which contains 25,000 IMDB movie reviews, each with a positive or negative sentiment label.
Next, read the tab-delimited file into Python. To do this, we can use the pandas package, introduced in the Titanic tutorial, which provides the read_csv function for easily reading and writing data files. If you haven't used pandas before, you may need to install it.
In [14]:
train = pd.read_csv("labeledTrainData.tsv", header=0, delimiter="\t", quoting=3)
In [15]:
train.shape # Get the shape, 25000 rows and 3 columns
Out[15]:
(25000, 3)
In [16]:
train.columns.values # Show the column names
Out[16]:
array(['id', 'sentiment', 'review'], dtype=object)
In [17]:
print(train["review"][0]) # Looking at some review
"With all this stuff going down at the moment with MJ i've started listening to his music, watching the odd documentary here and there, watched The Wiz and watched Moonwalker again. Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent. Moonwalker is part biography, part feature film which i remember going to see at the cinema when it was originally released. Some of it has subtle messages about MJ's feeling towards the press and also the obvious message of drugs are bad m'kay.<br /><br />Visually impressive but of course this is all about Michael Jackson so unless you remotely like MJ in anyway then you are going to hate this and find it boring. Some may call MJ an egotist for consenting to the making of this movie BUT MJ and most of his fans would say that he made it for the fans which if true is really nice of him.<br /><br />The actual feature film bit when it finally starts is only on for 20 minutes or so excluding the Smooth Criminal sequence and Joe Pesci is convincing as a psychopathic all powerful drug lord. Why he wants MJ dead so bad is beyond me. Because MJ overheard his plans? Nah, Joe Pesci's character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno, maybe he just hates MJ's music.<br /><br />Lots of cool things in this like MJ turning into a car and a robot and the whole Speed Demon sequence. Also, the director must have had the patience of a saint when it came to filming the kiddy Bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene.<br /><br />Bottom line, this movie is for people who like MJ on one level or another (which i think is most people). If not, then stay away. It does try and give off a wholesome message and ironically MJ's bestest buddy in this movie is a girl! Michael Jackson is truly one of the most talented people ever to grace this planet but is he guilty? Well, with all the attention i've gave this subject....hmmm well i don't know because people can be different behind closed doors, i know this for a fact. He is either an extremely nice but stupid guy or one of the most sickest liars. I hope he is not the latter."
In [21]:
# Initialize the BeautifulSoup object on a single movie review
example1 = BeautifulSoup(train["review"][0], 'html.parser')
print(example1.get_text())
"With all this stuff going down at the moment with MJ i've started listening to his music, watching the odd documentary here and there, watched The Wiz and watched Moonwalker again. Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent. Moonwalker is part biography, part feature film which i remember going to see at the cinema when it was originally released. Some of it has subtle messages about MJ's feeling towards the press and also the obvious message of drugs are bad m'kay.Visually impressive but of course this is all about Michael Jackson so unless you remotely like MJ in anyway then you are going to hate this and find it boring. Some may call MJ an egotist for consenting to the making of this movie BUT MJ and most of his fans would say that he made it for the fans which if true is really nice of him.The actual feature film bit when it finally starts is only on for 20 minutes or so excluding the Smooth Criminal sequence and Joe Pesci is convincing as a psychopathic all powerful drug lord. Why he wants MJ dead so bad is beyond me. Because MJ overheard his plans? Nah, Joe Pesci's character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno, maybe he just hates MJ's music.Lots of cool things in this like MJ turning into a car and a robot and the whole Speed Demon sequence. Also, the director must have had the patience of a saint when it came to filming the kiddy Bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene.Bottom line, this movie is for people who like MJ on one level or another (which i think is most people). If not, then stay away. It does try and give off a wholesome message and ironically MJ's bestest buddy in this movie is a girl! Michael Jackson is truly one of the most talented people ever to grace this planet but is he guilty? Well, with all the attention i've gave this subject....hmmm well i don't know because people can be different behind closed doors, i know this for a fact. He is either an extremely nice but stupid guy or one of the most sickest liars. I hope he is not the latter."
When considering how to clean the text, we should think about the data problem we are trying to solve. For many problems, it makes sense to remove punctuation. On the other hand, in this case, we are tackling a sentiment analysis problem, and it is possible that "!!!" or ":-(" could carry sentiment, and should be treated as words. In this tutorial, for simplicity, we remove the punctuation altogether, but it is something you can play with on your own.
Similarly, in this tutorial we will remove numbers, but there are other ways of dealing with them that make just as much sense. For example, we could treat them as words, or replace them all with a placeholder string such as "NUM".
To remove punctuation and numbers, we will use a package for dealing with regular expressions, called re. The package comes built-in with Python; no need to install anything. For a detailed description of how regular expressions work, see the package documentation. Now, try the following:
In [27]:
import re
# Use regular expressions to do a find-and-replace
# Basically we get rid of all the word that doesn't begin with a-Z or A-Z
letters_only = re.sub("[^a-zA-Z]", # The pattern to search for
" ", # The pattern to replace it with
example1.get_text() ) # The text to search
print(letters_only)
With all this stuff going down at the moment with MJ i ve started listening to his music watching the odd documentary here and there watched The Wiz and watched Moonwalker again Maybe i just want to get a certain insight into this guy who i thought was really cool in the eighties just to maybe make up my mind whether he is guilty or innocent Moonwalker is part biography part feature film which i remember going to see at the cinema when it was originally released Some of it has subtle messages about MJ s feeling towards the press and also the obvious message of drugs are bad m kay Visually impressive but of course this is all about Michael Jackson so unless you remotely like MJ in anyway then you are going to hate this and find it boring Some may call MJ an egotist for consenting to the making of this movie BUT MJ and most of his fans would say that he made it for the fans which if true is really nice of him The actual feature film bit when it finally starts is only on for minutes or so excluding the Smooth Criminal sequence and Joe Pesci is convincing as a psychopathic all powerful drug lord Why he wants MJ dead so bad is beyond me Because MJ overheard his plans Nah Joe Pesci s character ranted that he wanted people to know it is he who is supplying drugs etc so i dunno maybe he just hates MJ s music Lots of cool things in this like MJ turning into a car and a robot and the whole Speed Demon sequence Also the director must have had the patience of a saint when it came to filming the kiddy Bad sequence as usually directors hate working with one kid let alone a whole bunch of them performing a complex dance scene Bottom line this movie is for people who like MJ on one level or another which i think is most people If not then stay away It does try and give off a wholesome message and ironically MJ s bestest buddy in this movie is a girl Michael Jackson is truly one of the most talented people ever to grace this planet but is he guilty Well with all the attention i ve gave this subject hmmm well i don t know because people can be different behind closed doors i know this for a fact He is either an extremely nice but stupid guy or one of the most sickest liars I hope he is not the latter
In [26]:
lower_case = letters_only.lower() # Convert to lower case
words = lower_case.split() # Split into words
print(words)
['with', 'all', 'this', 'stuff', 'going', 'down', 'at', 'the', 'moment', 'with', 'mj', 'i', 've', 'started', 'listening', 'to', 'his', 'music', 'watching', 'the', 'odd', 'documentary', 'here', 'and', 'there', 'watched', 'the', 'wiz', 'and', 'watched', 'moonwalker', 'again', 'maybe', 'i', 'just', 'want', 'to', 'get', 'a', 'certain', 'insight', 'into', 'this', 'guy', 'who', 'i', 'thought', 'was', 'really', 'cool', 'in', 'the', 'eighties', 'just', 'to', 'maybe', 'make', 'up', 'my', 'mind', 'whether', 'he', 'is', 'guilty', 'or', 'innocent', 'moonwalker', 'is', 'part', 'biography', 'part', 'feature', 'film', 'which', 'i', 'remember', 'going', 'to', 'see', 'at', 'the', 'cinema', 'when', 'it', 'was', 'originally', 'released', 'some', 'of', 'it', 'has', 'subtle', 'messages', 'about', 'mj', 's', 'feeling', 'towards', 'the', 'press', 'and', 'also', 'the', 'obvious', 'message', 'of', 'drugs', 'are', 'bad', 'm', 'kay', 'visually', 'impressive', 'but', 'of', 'course', 'this', 'is', 'all', 'about', 'michael', 'jackson', 'so', 'unless', 'you', 'remotely', 'like', 'mj', 'in', 'anyway', 'then', 'you', 'are', 'going', 'to', 'hate', 'this', 'and', 'find', 'it', 'boring', 'some', 'may', 'call', 'mj', 'an', 'egotist', 'for', 'consenting', 'to', 'the', 'making', 'of', 'this', 'movie', 'but', 'mj', 'and', 'most', 'of', 'his', 'fans', 'would', 'say', 'that', 'he', 'made', 'it', 'for', 'the', 'fans', 'which', 'if', 'true', 'is', 'really', 'nice', 'of', 'him', 'the', 'actual', 'feature', 'film', 'bit', 'when', 'it', 'finally', 'starts', 'is', 'only', 'on', 'for', 'minutes', 'or', 'so', 'excluding', 'the', 'smooth', 'criminal', 'sequence', 'and', 'joe', 'pesci', 'is', 'convincing', 'as', 'a', 'psychopathic', 'all', 'powerful', 'drug', 'lord', 'why', 'he', 'wants', 'mj', 'dead', 'so', 'bad', 'is', 'beyond', 'me', 'because', 'mj', 'overheard', 'his', 'plans', 'nah', 'joe', 'pesci', 's', 'character', 'ranted', 'that', 'he', 'wanted', 'people', 'to', 'know', 'it', 'is', 'he', 'who', 'is', 'supplying', 'drugs', 'etc', 'so', 'i', 'dunno', 'maybe', 'he', 'just', 'hates', 'mj', 's', 'music', 'lots', 'of', 'cool', 'things', 'in', 'this', 'like', 'mj', 'turning', 'into', 'a', 'car', 'and', 'a', 'robot', 'and', 'the', 'whole', 'speed', 'demon', 'sequence', 'also', 'the', 'director', 'must', 'have', 'had', 'the', 'patience', 'of', 'a', 'saint', 'when', 'it', 'came', 'to', 'filming', 'the', 'kiddy', 'bad', 'sequence', 'as', 'usually', 'directors', 'hate', 'working', 'with', 'one', 'kid', 'let', 'alone', 'a', 'whole', 'bunch', 'of', 'them', 'performing', 'a', 'complex', 'dance', 'scene', 'bottom', 'line', 'this', 'movie', 'is', 'for', 'people', 'who', 'like', 'mj', 'on', 'one', 'level', 'or', 'another', 'which', 'i', 'think', 'is', 'most', 'people', 'if', 'not', 'then', 'stay', 'away', 'it', 'does', 'try', 'and', 'give', 'off', 'a', 'wholesome', 'message', 'and', 'ironically', 'mj', 's', 'bestest', 'buddy', 'in', 'this', 'movie', 'is', 'a', 'girl', 'michael', 'jackson', 'is', 'truly', 'one', 'of', 'the', 'most', 'talented', 'people', 'ever', 'to', 'grace', 'this', 'planet', 'but', 'is', 'he', 'guilty', 'well', 'with', 'all', 'the', 'attention', 'i', 've', 'gave', 'this', 'subject', 'hmmm', 'well', 'i', 'don', 't', 'know', 'because', 'people', 'can', 'be', 'different', 'behind', 'closed', 'doors', 'i', 'know', 'this', 'for', 'a', 'fact', 'he', 'is', 'either', 'an', 'extremely', 'nice', 'but', 'stupid', 'guy', 'or', 'one', 'of', 'the', 'most', 'sickest', 'liars', 'i', 'hope', 'he', 'is', 'not', 'the', 'latter']
Finally, we need to decide how to deal with frequently occurring words that don't carry much meaning. Such words are called "stop words"; in English they include words such as "a", "and", "is", and "the". Conveniently, there are Python packages that come with stop word lists built in. Let's import a stop word list from the Python Natural Language Toolkit (NLTK). You'll need to install the library if you don't already have it on your computer; you'll also need to install the data packages that come with it, as follows:
In [33]:
from nltk.corpus import stopwords # Import the stop word list
# Remove stop words from "words"
words = [w for w in words if not w in stopwords.words("english")]
print(words)
['stuff', 'going', 'moment', 'mj', 've', 'started', 'listening', 'music', 'watching', 'odd', 'documentary', 'watched', 'wiz', 'watched', 'moonwalker', 'maybe', 'want', 'get', 'certain', 'insight', 'guy', 'thought', 'really', 'cool', 'eighties', 'maybe', 'make', 'mind', 'whether', 'guilty', 'innocent', 'moonwalker', 'part', 'biography', 'part', 'feature', 'film', 'remember', 'going', 'see', 'cinema', 'originally', 'released', 'subtle', 'messages', 'mj', 'feeling', 'towards', 'press', 'also', 'obvious', 'message', 'drugs', 'bad', 'm', 'kay', 'visually', 'impressive', 'course', 'michael', 'jackson', 'unless', 'remotely', 'like', 'mj', 'anyway', 'going', 'hate', 'find', 'boring', 'may', 'call', 'mj', 'egotist', 'consenting', 'making', 'movie', 'mj', 'fans', 'would', 'say', 'made', 'fans', 'true', 'really', 'nice', 'actual', 'feature', 'film', 'bit', 'finally', 'starts', 'minutes', 'excluding', 'smooth', 'criminal', 'sequence', 'joe', 'pesci', 'convincing', 'psychopathic', 'powerful', 'drug', 'lord', 'wants', 'mj', 'dead', 'bad', 'beyond', 'mj', 'overheard', 'plans', 'nah', 'joe', 'pesci', 'character', 'ranted', 'wanted', 'people', 'know', 'supplying', 'drugs', 'etc', 'dunno', 'maybe', 'hates', 'mj', 'music', 'lots', 'cool', 'things', 'like', 'mj', 'turning', 'car', 'robot', 'whole', 'speed', 'demon', 'sequence', 'also', 'director', 'must', 'patience', 'saint', 'came', 'filming', 'kiddy', 'bad', 'sequence', 'usually', 'directors', 'hate', 'working', 'one', 'kid', 'let', 'alone', 'whole', 'bunch', 'performing', 'complex', 'dance', 'scene', 'bottom', 'line', 'movie', 'people', 'like', 'mj', 'one', 'level', 'another', 'think', 'people', 'stay', 'away', 'try', 'give', 'wholesome', 'message', 'ironically', 'mj', 'bestest', 'buddy', 'movie', 'girl', 'michael', 'jackson', 'truly', 'one', 'talented', 'people', 'ever', 'grace', 'planet', 'guilty', 'well', 'attention', 've', 'gave', 'subject', 'hmmm', 'well', 'know', 'people', 'different', 'behind', 'closed', 'doors', 'know', 'fact', 'either', 'extremely', 'nice', 'stupid', 'guy', 'one', 'sickest', 'liars', 'hope', 'latter']
In [40]:
def review_to_words( raw_review ):
# Function to convert a raw review to a string of words
# The input is a single string (a raw movie review), and
# the output is a single string (a preprocessed movie review)
#
# 1. Remove HTML
review_text = BeautifulSoup(raw_review, 'html.parser').get_text()
#
# 2. Remove non-letters
letters_only = re.sub("[^a-zA-Z]", " ", review_text)
#
# 3. Convert to lower case, split into individual words
words = letters_only.lower().split()
#
# 4. In Python, searching a set is much faster than searching
# a list, so convert the stop words to a set
stops = set(stopwords.words("english"))
#
# 5. Remove stop words
meaningful_words = [w for w in words if not w in stops]
#
# 6. Join the words back into one string separated by space,
# and return the result.
return( " ".join( meaningful_words ))
Let's clean all the data
In [39]:
print("Cleaning and parsing the training set movie reviews...\n")
clean_train_reviews = []
for i in range( 0, num_reviews ):
# If the index is evenly divisible by 1000, print a message
if( (i+1)%1000 == 0 ):
print("Review %d of %d\n" % ( i+1, num_reviews ))
clean_train_reviews.append( review_to_words( train["review"][i] ))
Cleaning and parsing the training set movie reviews...
Review 1000 of 25000
Review 2000 of 25000
Review 3000 of 25000
Review 4000 of 25000
Review 5000 of 25000
Review 6000 of 25000
Review 7000 of 25000
Review 8000 of 25000
Review 9000 of 25000
Review 10000 of 25000
Review 11000 of 25000
Review 12000 of 25000
Review 13000 of 25000
Review 14000 of 25000
Review 15000 of 25000
Review 16000 of 25000
Review 17000 of 25000
Review 18000 of 25000
Review 19000 of 25000
Review 20000 of 25000
Review 21000 of 25000
Review 22000 of 25000
Review 23000 of 25000
Review 24000 of 25000
Review 25000 of 25000
/Users/noppanit/.virtualenvs/ml/lib/python3.4/site-packages/bs4/__init__.py:166: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("html.parser"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.
To get rid of this warning, change this:
BeautifulSoup([your markup])
to this:
BeautifulSoup([your markup], "html.parser")
markup_type=markup_type))
Now that we have our training reviews tidied up, how do we convert them to some kind of numeric representation for machine learning? One common approach is called a Bag of Words. The Bag of Words model learns a vocabulary from all of the documents, then models each document by counting the number of times each word appears. For example, consider the following two sentences:
Sentence 1: "The cat sat on the hat"
Sentence 2: "The dog ate the cat and the hat"
From these two sentences, our vocabulary is as follows:
{ the, cat, sat, on, hat, dog, ate, and }
To get our bags of words, we count the number of times each word occurs in each sentence. In Sentence 1, "the" appears twice, and "cat", "sat", "on", and "hat" each appear once, so the feature vector for Sentence 1 is:
{ the, cat, sat, on, hat, dog, ate, and }
Sentence 1: { 2, 1, 1, 1, 1, 0, 0, 0 }
Similarly, the features for Sentence 2 are: { 3, 1, 0, 0, 1, 1, 1, 1}
In the IMDB data, we have a very large number of reviews, which will give us a large vocabulary. To limit the size of the feature vectors, we should choose some maximum vocabulary size. Below, we use the 5000 most frequent words (remembering that stop words have already been removed).
We'll be using the feature_extraction module from scikit-learn to create bag-of-words features. If you did the Random Forest tutorial in the Titanic competition, you should already have scikit-learn installed; otherwise you will need to install it.
In [42]:
print("Creating the bag of words...\n")
from sklearn.feature_extraction.text import CountVectorizer
# Initialize the "CountVectorizer" object, which is scikit-learn's
# bag of words tool.
vectorizer = CountVectorizer(analyzer = "word", \
tokenizer = None, \
preprocessor = None, \
stop_words = None, \
max_features = 5000)
# fit_transform() does two functions: First, it fits the model
# and learns the vocabulary; second, it transforms our training data
# into feature vectors. The input to fit_transform should be a list of
# strings.
train_data_features = vectorizer.fit_transform(clean_train_reviews)
# Numpy arrays are easy to work with, so convert the result to an
# array
train_data_features = train_data_features.toarray()
Creating the bag of words...
It has 25,000 rows and 5,000 features (one for each vocabulary word).
Note that CountVectorizer comes with its own options to automatically do preprocessing, tokenization, and stop word removal -- for each of these, instead of specifying "None", we could have used a built-in method or specified our own function to use. See the function documentation for more details. However, we wanted to write our own function for data cleaning in this tutorial to show you how it's done step by step.
Now that the Bag of Words model is trained, let's look at the vocabulary:
In [45]:
vocab = vectorizer.get_feature_names()
print(vocab)
# Sum up the counts of each vocabulary word
dist = np.sum(train_data_features, axis=0)
# For each, print the vocabulary word and the number of times it
# appears in the training set
for tag, count in zip(vocab, dist):
print(count, tag)
['abandoned', 'abc', 'abilities', 'ability', 'able', 'abraham', 'absence', 'absent', 'absolute', 'absolutely', 'absurd', 'abuse', 'abusive', 'abysmal', 'academy', 'accent', 'accents', 'accept', 'acceptable', 'accepted', 'access', 'accident', 'accidentally', 'accompanied', 'accomplished', 'according', 'account', 'accuracy', 'accurate', 'accused', 'achieve', 'achieved', 'achievement', 'acid', 'across', 'act', 'acted', 'acting', 'action', 'actions', 'activities', 'actor', 'actors', 'actress', 'actresses', 'acts', 'actual', 'actually', 'ad', 'adam', 'adams', 'adaptation', 'adaptations', 'adapted', 'add', 'added', 'adding', 'addition', 'adds', 'adequate', 'admire', 'admit', 'admittedly', 'adorable', 'adult', 'adults', 'advance', 'advanced', 'advantage', 'adventure', 'adventures', 'advertising', 'advice', 'advise', 'affair', 'affect', 'affected', 'afford', 'aforementioned', 'afraid', 'africa', 'african', 'afternoon', 'afterwards', 'age', 'aged', 'agent', 'agents', 'ages', 'aging', 'ago', 'agree', 'agreed', 'agrees', 'ah', 'ahead', 'aid', 'aids', 'aim', 'aimed', 'ain', 'air', 'aired', 'airplane', 'airport', 'aka', 'akshay', 'al', 'alan', 'alas', 'albeit', 'albert', 'album', 'alcohol', 'alcoholic', 'alec', 'alert', 'alex', 'alexander', 'alfred', 'alice', 'alicia', 'alien', 'aliens', 'alike', 'alison', 'alive', 'allen', 'allow', 'allowed', 'allowing', 'allows', 'almost', 'alone', 'along', 'alongside', 'already', 'alright', 'also', 'alternate', 'although', 'altman', 'altogether', 'always', 'amanda', 'amateur', 'amateurish', 'amazed', 'amazing', 'amazingly', 'ambiguous', 'ambitious', 'america', 'american', 'americans', 'amitabh', 'among', 'amongst', 'amount', 'amounts', 'amusing', 'amy', 'analysis', 'ancient', 'anderson', 'andre', 'andrew', 'andrews', 'andy', 'angel', 'angela', 'angeles', 'angels', 'anger', 'angle', 'angles', 'angry', 'animal', 'animals', 'animated', 'animation', 'anime', 'ann', 'anna', 'anne', 'annie', 'annoyed', 'annoying', 'another', 'answer', 'answers', 'anthony', 'anti', 'antics', 'antonioni', 'antwone', 'anybody', 'anymore', 'anyone', 'anything', 'anyway', 'anyways', 'anywhere', 'apart', 'apartment', 'ape', 'apes', 'appalling', 'apparent', 'apparently', 'appeal', 'appealing', 'appear', 'appearance', 'appearances', 'appeared', 'appearing', 'appears', 'appreciate', 'appreciated', 'appreciation', 'approach', 'appropriate', 'april', 'area', 'areas', 'aren', 'arguably', 'argue', 'argument', 'arm', 'armed', 'arms', 'army', 'arnold', 'around', 'arrested', 'arrival', 'arrive', 'arrived', 'arrives', 'arrogant', 'art', 'arthur', 'artificial', 'artist', 'artistic', 'artists', 'arts', 'ashamed', 'ashley', 'asian', 'aside', 'ask', 'asked', 'asking', 'asks', 'asleep', 'aspect', 'aspects', 'ass', 'assassin', 'assault', 'assigned', 'assistant', 'associated', 'assume', 'assumed', 'astaire', 'astonishing', 'atlantis', 'atmosphere', 'atmospheric', 'atrocious', 'attached', 'attack', 'attacked', 'attacks', 'attempt', 'attempted', 'attempting', 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3078 whole
82 wholly
84 whoopi
986 whose
118 wicked
281 wide
87 widely
155 widmark
105 widow
2140 wife
432 wild
80 wilderness
596 william
344 williams
85 willie
320 willing
92 willis
200 wilson
491 win
274 wind
257 window
102 winds
177 wing
232 winner
348 winning
164 wins
137 winter
105 winters
89 wisdom
359 wise
957 wish
95 wished
154 wishes
85 wishing
234 wit
322 witch
92 witches
832 within
3267 without
210 witness
88 witnessed
89 witnesses
273 witty
105 wives
95 wizard
120 wolf
2795 woman
1819 women
1683 won
1038 wonder
126 wondered
1656 wonderful
324 wonderfully
358 wondering
125 wonders
101 wont
80 woo
271 wood
330 wooden
400 woods
240 woody
926 word
885 words
98 wore
4372 work
635 worked
129 worker
165 workers
794 working
1278 works
3835 world
143 worlds
88 worn
117 worried
142 worry
1468 worse
2731 worst
2277 worth
126 worthless
187 worthwhile
362 worthy
12436 would
1059 wouldn
94 wound
84 wounded
431 wow
87 wrap
92 wrapped
127 wreck
103 wrestling
79 wretched
670 write
1153 writer
661 writers
94 writes
1304 writing
1616 written
1821 wrong
573 wrote
85 ww
158 wwii
103 ya
82 yard
462 yeah
2362 year
4516 years
92 yelling
106 yellow
1535 yes
116 yesterday
2753 yet
809 york
3660 young
503 younger
275 youth
384 zero
85 zizek
740 zombie
518 zombies
147 zone
At this point, we have numeric training features from the Bag of Words and the original sentiment labels for each feature vector, so let's do some supervised learning! Here, we'll use the Random Forest classifier that we introduced in the Titanic tutorial. The Random Forest algorithm is included in scikit-learn (Random Forest uses many tree-based classifiers to make predictions, hence the "forest"). Below, we set the number of trees to 100 as a reasonable default value. More trees may (or may not) perform better, but will certainly take longer to run. Likewise, the more features you include for each review, the longer this will take.
In [ ]:
print("Training the random forest...")
from sklearn.ensemble import RandomForestClassifier
# Initialize a Random Forest classifier with 100 trees
forest = RandomForestClassifier(n_estimators = 100)
# Fit the forest to the training set, using the bag of words as
# features and the sentiment labels as the response variable
#
# This may take a few minutes to run
forest = forest.fit( train_data_features, train["sentiment"] )
All that remains is to run the trained Random Forest on our test set and create a submission file. If you haven't already done so, download testData.tsv from the Data page. This file contains another 25,000 reviews and ids; our task is to predict the sentiment label.
Note that when we use the Bag of Words for the test set, we only call "transform", not "fit_transform" as we did for the training set. In machine learning, you shouldn't use the test set to fit your model, otherwise you run the risk of overfitting. For this reason, we keep the test set off-limits until we are ready to make predictions.
In [ ]:
# Read the test data
test = pd.read_csv("testData.tsv", header=0, delimiter="\t", quoting=3 )
# Verify that there are 25,000 rows and 2 columns
print(test.shape)
# Create an empty list and append the clean reviews one by one
num_reviews = len(test["review"])
clean_test_reviews = []
print "Cleaning and parsing the test set movie reviews...\n"
for i in xrange(0,num_reviews):
if( (i+1) % 1000 == 0 ):
print "Review %d of %d\n" % (i+1, num_reviews)
clean_review = review_to_words( test["review"][i] )
clean_test_reviews.append( clean_review )
# Get a bag of words for the test set, and convert to a numpy array
test_data_features = vectorizer.transform(clean_test_reviews)
test_data_features = test_data_features.toarray()
# Use the random forest to make sentiment label predictions
result = forest.predict(test_data_features)
# Copy the results to a pandas dataframe with an "id" column and
# a "sentiment" column
output = pd.DataFrame( data={"id":test["id"], "sentiment":result} )
# Use pandas to write the comma-separated output file
output.to_csv( "Bag_of_Words_model.csv", index=False, quoting=3 )
Content source: noppanit/machine-learning
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