A ML and NLP based Framework for Sentiment Analysis on Bigdata

Abstract

Big data as multiple sources and social media is one of them. Such data is rich in opinion of people and needs automated approach with Natural Language Processing (NLP) and Machine Learning (ML) to obtain and summarize social feedback. With ML as an integral part of Artificial Intelligence (AI), machines can demonstrate intelligence exhibited by humans. ML is widely used in different domains. With proliferation of Online Social Networks (OSNs), people of all walks of life exchange their views instantly. Thus they became platforms where opinions or people are available. In other words, social feedback on products and services are available. For instance, Twitter produces large volumes of such data which is of much use to enterprises to garner Business Intelligence (BI) useful to make expert decisions. In addition to the traditional feedback systems, the feedback (opinions) over social networks provide depth in the intelligence to revise strategies and policies. Sentiment analysis is the phenomenon which is employed to analyze opinions and classify them into positive, negative and neutral. Existing studies usually treated overall sentiment analysis and aspect-based sentiment analysis in isolation, and then introduce a variety of methods to analyse either overall sentiments or aspect-level sentiments, but not both. Usage of probabilistic topic model is a novel approach in sentiment analysis. In this paper, we proposed a framework for comprehensive analysis of overall and aspect-based sentiments. The framework is realized with aspect based topic modelling for sentiment analysis and ensemble learning algorithms. It also employs many ML algorithms with supervised learning approach. Benchmark datasets used in international SemEval conferences are used for empirical study. Experimental results revealed the efficiency of the proposed framework over the state of the art.