A HYBRID SENTIMENT ANALYSIS APPROACH USING BLACK WIDOW OPTIMIZATION BASED FEATURE SELECTION

This paper proposes a novel hybrid framework with BWO based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used PSO and GAbased feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analysed using performance metrices such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.