基于情感分析和Transformer模型的微博谣言检测

针对微博文本以实现谣言检测为目标，深度挖掘微博正文内容的语义信息，并且着重强调用户在微博评论中体现的情感倾向性，提升谣言识别效果。为提高谣言检测的准确率，采取基于XLNet的词嵌入方法，使用Transformer的Encoder的模型提取微博正文内容的语义特征，并结合BiLSTM+Attention网络实现微博评论的情感特征的提取，将2种特征向量进行拼接融合，进一步丰富神经网络的输入特征，之后输出微博事件的分类结果，进而实现微博谣言检测。实验结果显示，该模型对谣言识别的正确率达到94.8%。

Microblog Rumor Detection Based on Sentiment Analysis and Transformer Model

Aiming at realizing the rumor detection on microblog, this paper deeply excavates the semantic information of the body content of microblog, and emphasizes the emotional tendency reflected by users in microblog comments, so as to improve the effect of rumor identification. In order to improve the rumor detection accuracy, based on XLNet word embedding method, the Transformer’s Encoder model is used to extract the semantic features of microblog body content. Combined with the BiLSTM+Attention network, the emotional feature extraction of microblog comments is realized. Two kinds of feature vectors are spliced and fused to further enrich the input features of neural network. Then, the microblog event classification results are output, and the microblog rumors detection is achieved. The experimental results show that the accuracy of the model in rumor recognition reaches 94.8%.