Sentiment Analysis of Comment Texts on Converged Media Platforms based on BERT Model

H. Liang, Baihui Tang, Sanxing Cao
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Abstract

The development of mobile Internet, smart terminals and cloud computing has promoted the advent of the era of media convergence. And Converged Media Centers in various regions had gradually been established, assuming the responsibility for disseminating news and current affairs and guiding and supervising public opinion. The study of comment texts under the converged media platforms is more targeted. In addition, this research is of great significance to the supervision and guidance of local public. This paper applies the BERT (Bidirectional Encoder Representation from Transformers) model to the sentiment analysis of comment texts on a converged media platform. In this experiment, the data used are the comment texts collected by our laboratory under the videos, which were posted by a part of the Converged Media Center accounts on the Douyin platform. By fine-tuning the pre-trained BERT model, and then the results obtained are classified and processed through the constructed neural network model. The accuracy of the validation set is 85.83%. Compared with other models, this method has a significant improvement in classification accuracy. When it is applied to a converged media platform, it is very possible to obtain correct classification results.
基于BERT模型的融合媒体平台评论文本情感分析
移动互联网、智能终端、云计算的发展,推动了媒体融合时代的到来。各地逐步建立融合媒体中心,承担新闻时事传播和舆论引导监督的职责。融合媒体平台下的评论文本研究更具针对性。此外,本研究对地方公众的监督和指导具有重要意义。本文将BERT (Bidirectional Encoder Representation from Transformers)模型应用于融合媒体平台上评论文本的情感分析。在本实验中,使用的数据是我们实验室在视频下收集的评论文本,这些评论文本是由抖音平台上部分融合媒体中心账号发布的。通过对预训练的BERT模型进行微调,然后通过构建的神经网络模型对得到的结果进行分类和处理。验证集的准确率为85.83%。与其他模型相比,该方法在分类精度上有显著提高。当将其应用于融合媒体平台时,极有可能获得正确的分类结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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