Identifying negativity factors from social media text corpus using sentiment analysis method. (arXiv:2107.02175v1 [cs.CL])

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.

Source: https://arxiv.org/abs/2107.02175

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