Identifying negativity factors from social media text corpus using sentiment analysis method

TL;DR

This paper presents a hierarchical sentiment analysis approach that classifies social media comments into negative or positive, then further categorizes negative comments into specific classes to better understand underlying reasons for negativity.

Contribution

The study introduces a hierarchical classification method for negative social media comments, linking them to detailed categories based on expert input, and evaluates multiple machine learning algorithms for accuracy.

Findings

Hierarchical classification improves understanding of negative comments.

Machine learning algorithms achieve varying accuracy levels.

Eight specific negative comment classes identified and analyzed.

Abstract

Automatic sentiment analysis play vital role in decision making. Many organizations spend a lot of budget to understand their customer satisfaction by manually going over their feedback/comments or tweets. Automatic sentiment analysis can give overall picture of the comments received against any event, product, or activity. Usually, the comments/tweets are classified into two main classes that are negative or positive. However, the negative comments are too abstract to understand the basic reason or the context. organizations are interested to identify the exact reason for the negativity. In this research study, we hierarchically goes down into negative comments, and link them with more classes. Tweets are extracted from social media sites such as Twitter and Facebook. If the sentiment analysis classifies any tweet into negative class, then we further try to associates that negative comments with more possible negative classes. Based on expert opinions, the negative comments/tweets are further classified into 8 classes. Different machine learning algorithms are evaluated and their accuracy are reported.