A Combination of Machine Learning and Lexicon Based Techniques for Sentiment Analysis

Today millions of web users put their opinions on the internet about various topics. Development of methods that automatically categorize these opinions to positive, negative or neutral is important. Opinion mining or sentiment analysis is known as mining of behavior, opinions and sentiments of the text, chat, etc. using natural language processing and information retrieval methods. The paper is aimed to study the effect of combining machine learning methods in a meta-classifier for sentiment analysis. The machine learning methods use the output of lexicon-based techniques. In this way, the score of SentiWordNet dictionary, Liu's sentiment list, SentiStrength and sentimental words ratios are computed and used as the input of machine learning techniques. Adjectives, adverbs and verbs of an opinion are used for opinion modeling and score of these words are extracted from lexicons. Experimental results show that the meta-classifier improve the accuracy of classification 0.9% and 1.09% for Amazon and IMDB reviews in comparison with the four machine learning techniques evaluated here.