Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method

TitleSentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method
Publication TypeJournal Article
Year of PublicationIn Press
AuthorsKumar, K. H. M., B. S. Harish, and H. K. Darshan
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
IssueIn Press
VolumeIn Press
NumberIn Press
Date Published12/2018

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, Naïve 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.

KeywordsClassification, Hybrid Features, Sentiment Analysis, Short Text
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