Sentiment Analysis Using Hybrid Machine Learning Technique

Abstract

It is observed that consumers often share their opinion, views or feeling about any term used on social network in the form of reviews, comments or feedback. Those feedbacks given by end users have a great impact for evolution of new version of any product. Due to this trend in social media in recent years, sentiment analysis has become an important concern for theoreticians and practitioners Moreover reviews are often written in natural language and are mostly unstructured. Thus, to obtain any meaningful information from these reviews, it needs to be processed. Due to large size of data it is impossible to process this information manually. Hence machine learning algorithms are considered for analysis. Since data are unstructured in nature, unsupervised machine learning algorithm can be helpful in solving this problem. But unsupervised methods have less accuracy; hence not acceptable. In this study, a hybrid machine learning approach is adopted to automatically find the requirements for next version of software. Also some reviews neither belong to positive cluster nor to negative. They mixed reaction or feeling about some topics. Those problem associated with NLP is solved using hybrid technique of the fuzzy c-means and ANN. Moreover in this study, different methods of unsupervised machine leaning algorithm are implemented and their results are compared with each other. The best outcome is used to train the neural network. By using this hybridization technique, accuracy gets increased. And in later stage, this technique is applied to find the new requirement of product.