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.

在社交媒体的世界中，维护声誉资本至关重要，但是在自动化可靠的在线声誉分析方面仍面临挑战。因此我们提出了一种基于 BERT 的声誉分析方法，并在 RepLab 2013 数据集上进行了评估。相比之前的工作，我们的准确度提高了 5.8％，平衡准确度提高了 26.9％，F 分数提高了 21.8％。