Identifying negativity factors from social media text corpus using sentiment analysis method
This work addresses the need for organizations to pinpoint exact causes of customer dissatisfaction from social media, though it is incremental as it builds on existing sentiment analysis methods.
The study tackled the problem of understanding the specific reasons behind negative social media comments by extending sentiment analysis to classify negative tweets into eight detailed categories, achieving reported accuracies with various machine learning algorithms.
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.