A BERT-based Deep Learning Approach for Reputation Analysis in Social
Media
- URL: http://arxiv.org/abs/2211.01954v1
- Date: Sun, 23 Oct 2022 02:04:03 GMT
- Title: A BERT-based Deep Learning Approach for Reputation Analysis in Social
Media
- Authors: Mohammad Wali Ur Rahman, Sicong Shao, Pratik Satam, Salim Hariri,
Chris Padilla, Zoe Taylor and Carlos Nevarez
- Abstract summary: We propose a novel reputation analysis approach based on the popular language model BERT (Bidirectional Representations from Transformers)
The proposed approach was evaluated on the polarity reputational 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.
- Score: 1.6624933615451845
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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