Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination

TL;DR

This paper compares lexicon-based and machine learning methods for Twitter sentiment analysis, demonstrating that combining these approaches and using cost-sensitive classifiers enhances classification accuracy.

Contribution

It introduces a new ensemble method combining lexicon scores with machine learning and evaluates performance improvements on a benchmark dataset.

Findings

Machine learning methods outperform lexicon-based methods.

Enhancing lexicons with social media slang improves accuracy.

Combined ensemble method yields more precise sentiment classification.

Abstract

This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combinations and show that enhancing sentiment lexicons with emoticons, abbreviations and social-media slang expressions increases the accuracy of lexicon-based classification for Twitter. We discuss the importance of feature generation and feature selection processes for machine learning sentiment classification. To quantify the performance of the main sentiment analysis methods over Twitter we run these algorithms on a benchmark Twitter dataset from the SemEval-2013 competition, task 2-B. The results show that machine learning method based on SVM and Naive Bayes classifiers outperforms the lexicon method. We present a new ensemble method that uses a lexicon based sentiment score as input feature for the machine learning approach. The combined method proved to produce more precise classifications. We also show that employing a cost-sensitive classifier for highly unbalanced datasets yields an improvement of sentiment classification performance up to 7%.