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.