A COMPARATIVE STUDY OF STATISTICAL AND NATURAL LANGUAGE PROCESSING TECHNIQUES FOR SENTIMENT ANALYSIS

Abstract

Sentiment analysis has emerged as one of the most powerful tools in business intelligence. With the aim of proposing an effective sentiment analysis technique, we have performed experiments on analyzing the sentiments of 3,424 tweets using both statistical and natural language processing (NLP) techniques as part of our background study. For statistical technique, machine learning algorithms such as Support Vector Machines (SVMs), decision trees and Naïve Bayes have been explored. The results show that SVM consistently outperformed the rest in both classifications. As for sentiment analysis using NLP techniques, we used two different tagging methods for part-of-speech (POS) tagging. Subsequently, the output is used for word sense disambiguation (WSD) using WordNet, followed by sentiment identification using SentiWordNet. Our experimental results indicate that adjectives and adverbs are sufficient to infer the sentiment of tweets compared to other combinations. Comparatively, the statistical approach records higher accuracy than the NLP approach by approximately 17%.