Abstract
Human disposition has always influenced by others suggestion and reviews. People are always eager to know other's reviews for their profit but, every website contains a very large amount of review text, the average human reader will have trouble in identifying relevant sites, extracting and abstracting the reviews so they cannot reach to the right decision in less time that is why automated sentiment analysis systems are required. In the proposed approach, heterogeneous features such as machine learning based and Lexicon based features and supervised learning algorithms like Naive Bayes (NB) and Linear Support Vector Machine (LSVM) used to build the system model. From implementation and observation, conclude that using proposed heterogeneous features and hybrid approach can get an accurate sentiment analysis system compared to other baseline system. In future for big data, we can use these heterogeneous features for bulding advance and more accurate models using Deep Learning (DL) algorithms.
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