A BERT-based Deep Learning Approach for Reputation Analysis in Social Media

TL;DR

This paper introduces a BERT-based deep learning method for analyzing social media reputation, significantly improving accuracy and F-score in reputation polarity detection compared to prior approaches.

Contribution

The paper presents a novel BERT-based approach for online reputation analysis in social media, achieving notable performance improvements over existing methods.

Findings

5.8% accuracy improvement

26.9% balanced accuracy improvement

21.8% F-score improvement

Abstract

Social media has become an essential part of the modern lifestyle, with its usage being highly prevalent. This has resulted in unprecedented amounts of data generated from users in social media, such as users' attitudes, opinions, interests, purchases, and activities across various aspects of their lives. Therefore, in a world of social media, where its power has shifted to users, actions taken by companies and public figures are subject to constantly being under scrutiny by influential global audiences. As a result, reputation management in social media has become essential as companies and public figures need to maintain their reputation to preserve their reputation capital. However, domain experts still face the challenge of lacking appropriate solutions to automate reliable online reputation analysis. To tackle this challenge, we proposed a novel reputation analysis approach based on the popular language model BERT (Bidirectional Encoder Representations from Transformers). The proposed approach was evaluated on the reputational polarity task using RepLab 2013 dataset. Compared to previous works, we achieved 5.8% improvement in accuracy, 26.9% improvement in balanced accuracy, and 21.8% improvement in terms of F-score.