01817nas a2200229   4500000000100000000000100001008004100002260001200043653001900055653002300074653002000097653001500117100001500132700001800147700001600165245008400181856009700265300001200362490000600374520119300380022001401573       2019                            d  c06/201910aClassification10aSentiment Analysis10aHybrid Features10aShort Text1 aB S Harish1 aKeerthi Kumar1 aH K Darshan00aSentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method  uhttps://www.ijimai.org/journal/sites/default/files/files/2018/12/ijimai_5_5_13_pdf_67503.pdf  a109-1140 v53 aSocial 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.  a1989-1660