基于情感分析的虚假信息识别模型

Fake News Recognition Model Based on Sentiment Analysis

以微博为代表的社交平台已经成为人们获取信息和发布信息的重要途径,也成为虚假信息滋生的温床。包含虚假信息的微博往往含有明显的情感偏向。文章从情感分析角度出发,提出一种Bert模型结合BI-LSTM模型的虚假信息识别模型(LableBert模型):首先利用情感词典给情感词添加权重,改进Bert的预训练任务,以提升对于隐式情感特征的提取能力,并批量标注被掩盖单词的文本情感极性,以加强对文本中上下文的情感特征获取能力;然后结合BI-LSTM模型进行全连接操作,从而识别虚假信息。实验结果表明,该模型的准确率达到了91.36%,F1值达到了91.03%,相比原Bert模型,该模型的准确率和F1值都有所提升。

The social platform represented by Weibo has become an important way for people to obtain and release information,and it has also become a breeding ground for false information.Weibo containing false information often contains obvious emotional bias.From the perspective of sentiment analysis,this paper proposes the LableBert model:a false information recognition model based on Bert combined with BI-LSTM.It uses the emotional dictionary to add weights to emotional words,and improves Bert's pre-training tasks,and enhances the model's ability to extract implicit emotional features.And batch mark the text emotional polarity of the masked words,and strengthen the model's ability to acquire emotional features of the text context,and combine BI-LSTM to identify false information.Experimental results show that the accuracy of the model has reached 91.36%,and the F1 value has reached 91.03%.Compared with the original Bert model,the accuracy and F1 value of the model have been improved.