Abstract
Social Networking sites have become popular and common places for sharing wide range of emotions through short texts. These emotions include happiness, sadness, anxiety, fear, etc. Analyzing short texts helps in identifying the sentiment expressed by the crowd. Sentiment Analysis on IMDb movie reviews identifies the overall sentiment or opinion expressed by a reviewer towards a movie. Many researchers are working on pruning the sentiment analysis model that clearly identifies and distinguishes between a positive review and a negative review. In the proposed work, we show that the use of Hybrid features obtained by concatenating Machine Learning features (TF, TF-IDF) with Lexicon features (Positive-Negative word count, Connotation) gives better results both in terms of accuracy and complexity when tested against classifiers like SVM, Naive Bayes, KNN and Maximum Entropy. The proposed model clearly differentiates between a positive review and negative review. Since understanding the context of the reviews plays an important role in classification, using hybrid features helps in capturing the context of the movie reviews and hence increases the accuracy of classification.
Highlights
SOCIAL media has become an integral part of human living in recent days
Regularized Locality Preserving Indexing (RLPI) helps in handling large number of features which can be further reduced to smaller dimension feature space
Naïve Bayes works on the principle of independence of features and calculates the probability of a review belonging to particular class using Bayes theorem
Summary
SOCIAL media has become an integral part of human living in recent days. People want to share each and every happening of their life on social media. The text plays a vital aspect in information shared, where users share their opinions on trending topics, politics, movie reviews, etc These opinions which people share on social networking sites are generally known as Short Texts (ST) because of its length [1]. SA played an important role in the US Presidential Elections 2016 [3] People shared their likes and dislikes regarding a particular political party on micro-blogs such as Twitter and Facebook. People generally look into blogs, review sites like IMDb to know about movie cast, crew, review and ratings It is the Word of Mouth that brings the audience to the theatres; reviews play a prominent role in this regard.
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