Authors: Jeshuran Thangaraj, Vaishnavi Mukundhan, Prateek Srivastava

Sentiment Analysis:

Sentiment analysis or Opinion Mining is a process of extracting the opinions in a text rather than the topic of the document. The text would have sentences that are either facts or opinions. We classify the opinions into three categories: Positive, Negative and Neutral.

Datasets:

The existing implementation^[1] uses the Kaggle Dataset. In our implementation we have used the below two datasets:

1. Kaggle IMDB
2. Large Movie Review Dataset (Stanford Movie Review Dataset)

Existing Implementations:

• SentiWordNet^[2]:

• SentiWordNet is a resource used to perform Opinion Mining.
• This resource considers the synset instead of the term as each of the sense would have different opinions.
• It gives a positive, negative and objective score to each word which totals to 1.
• SentiWordNet uses Eight ternary classifiers to find the opinion of each word.

• Naives Bayes to Classify the Opinions^[3]:

• In this approach, they extract a feature vector from the text and directly classify them using the NLTK's Naive Bayes Classifier.
• This can also be done with other classifiers like SVM.

Our Approaches:

After reviewing multiple approaches, we finally narrowed down on the following two implementations which could be reviewed in the time available.

• BiGram Model:

  • This is an add on to the pre existing unigram model implementation^[1]. In this we first pre process the text by cleansing, Tokenizing, Lemmatizing and POS tagging.
  • Each word is given a score between 0-1 by passing it to the SENTIWORDNET.
  • We changed the scoring mechanism from a unigram model to a bigram model by taking the MAX absolute score of either model.
  • Accuracy :
    • Bigram 61%
    • Unigram 66%

• Building a WordNet for our Datasets:

  • We started under the assumption that training on similar data will yield better results.
  • Trained a unigram model using 25000 movie reviews.
  • We extracted the ADJ and ADV POS-tags from the training corpus and built a frequency distribution for each word based on its occurrence in positive and negative reviews.
  • It was then used on our test set to predict opinions.
  • Accuracy:
    • Negative Test set 75.4%
    • Positive Test set 67%

Future Approaches:

• We would like to figure out why the Bigram model is giving us a lesser accuracy than the unigram model.
• For the second approach we would use Bigram
• We would also like to try using LSTM.

Code Link

• https://github.com/prateek22sri/sentimentAnalysis

References:

• http://nlpforhackers.io/sentiment-analysis-intro/
• http://sentiwordnet.isti.cnr.it/
• https://streamhacker.com/tag/bigrams/