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Multi-domain Sentiment Analysis

Multi-domain Sentiment Analysis using Natural Language Processing techniques.

Abstract

This is a sentiment analysis model aimed at determining whether a product review is positive or negative using natural language processing techniques. The model was built with a Long-Short Term Memory recurrent neural network and scored 77% accuracy on test data.

Dataset

The dataset used is the Multi-Domain Sentiment Dataset provided by Mark Dredze and John Blitzer. The Multi-Domain Sentiment Dataset contains product reviews taken from Amazon.com from 4 product types (domains): Kitchen, Books, DVDs, and Electronics. Each domain has two thousand reviews in total.

The dataset split the reviews in 4 different folders (folder for each domain) and each folder contained positive reviews file, negative reviews file, and an optional unlabeled reviews file. Each file contains a pseudo-XML scheme for encoding the reviews.

A negative review example:

<review>
  <unique_id>
    0312355645:horrible_book,_horrible.:mark_gospri
  </unique_id>
  <asin>
    0312355645
  </asin>
  <product_name>
    Running with Scissors: A Memoir: Books: Augusten Burroughs
  </product_name>
  <product_type>
    books
  </product_type>
  <helpful>
    4 of 9
  </helpful>
  <rating>
    1.0
  </rating>
  <title>
    Horrible book, horrible.
   </title>
  <date>
    November 14, 2006
  </date>
  <reviewer>
    Mark Gospri
  </reviewer>
  <reviewer_location>

  </reviewer_location>
  <review_text>
    THis book was horrible.  If it was possible to rate it lower than one star i would have.  I am an avid reader and picked this book up after my mom had gotten it from a     friend.  I read half of it, suffering from a headache the entire time, and then got to the part about the relationship the 13 year old boy had with a 33 year old man       and i lit this book on fire.  One less copy in the world...don't waste your money.

    I wish i had the time spent reading this book back so i could use it for better purposes.  THis book wasted my life
  </review_text>
</review>

Cleaning

The reading process started and only the review text was taken into consideration. Regular expressions were used to extract the review text from files.

# Regex for reviews extraction
regex_review = re.compile("<review_text>.+?<\/review_text>", flags=re.DOTALL)

Cleaning process included:

  • Removing the <review_text> tags
  • Normalizing letter case
  • Removing URLs and email addresses
  • Removing Punctuation
  • Removing stop words
  • Fixed some offensive words that were altered with symbols to avoid being detected (They are important in our analysis)

Looking for useful insights

Three domains (books, DVD, and electronics) were taken as training data and the last domain (kitchen & housewares) was used for testing. We had 6,000 training reviews in total, split into 50% positive reviews and 50% negative reviews which makes the data perfectly balanced.

Count of words in reviews HistogramCount of words in reviews Histogram with 20 words bin size except the last bin 300-2000

I found that that longest review was 1,942 words which is very large for an LSTM network to handle. While most reviews (76.9%) are 100 words or less. I decided to go with a sequence size of 125 words for the LSTM network. (Decision was taken according to weighted mean and neglecting reviews above 300 words)

The Model

The model consists of 1 embedding layer, 2 LSTM layers, and 1 output dense layer. For the embedding layer, I used weights from GloVe Twitter (200d) model to obtain vector representations for words to be feed to the neural network.

The reason for choosing the GloVe Twitter model is that it is trained on informal, slang, spoken English (aka colloquial English) which would be very helpful in our case where reviews could be grammatically incorrect, or include misspelled words.

The model scored 89.6% accuracy on train data, 78.7% on validation data, and 77% on test data. Check model summary below:

_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 embedding (Embedding)       (None, 125, 200)          8593600   
                                                                 
 lstm (LSTM)                 (None, 125, 30)           27720     
                                                                 
 lstm_1 (LSTM)               (None, 30)                7320      
                                                                 
 dense (Dense)               (None, 1)                 31        
                                                                 
=================================================================
Total params: 8,628,671
Trainable params: 35,071
Non-trainable params: 8,593,600

Examples

# Straight forward positive
lstm_predict("I really recommend this book")
output: Positive Review

# Tricky positive
lstm_predict("The dvd included a big poster of my favorite hero. I just can't wait for the second episode")
output: Positive Review

# Straight forward negative
lstm_predict("I don't know what the hell did i just read, the book is full nonsense")
output: Negative Review

# Tricky negative
lstm_predict("The book is very huge with too much unnecessary details that could have been omitted. Just buy another book!")
output: Negative Review

Tags: Python, Scikit Learn, NLTK, Gensim, NLP, LSTM, RNN, NN, TensorFlow, Regex, Word Embeddings, Word2Vec