基于Attention-Bi-LSTM的微博评论情感分析研究
Attention-Bi-LSTM Based Analysis of Weibo Comments
DOI: 10.12677/CSA.2020.1012252, PDF,    国家科技经费支持
作者: 王 彬, 蒋鸿玲*：北京信息科技大学信息管理学院，北京；吴 槟：中国科学院信息工程研究所，信息安全国家重点实验室，北京;中国科学院大学，网络空间安全学院，北京
关键词: 情感分析；Word2vec；Attention；Bi-LSTM；Emotion Analysis； Word2vec； Attention； Bi-LSTM
摘要: 短文本情感分析，在舆情监控和商业上有很多重要应用。以微博评论文本为研究对象，通过对微博评论文本进行分词、去除停用词，并使用Word2vec进行词向量训练得到词向量，并在Bi-LSTM中引入Attention机制，对Bi-LSTM双向处理后的结果进行加权进行输出。实验结果表明，Attention-Bi-LSTM与Bi-LSTM相比能有效识别出情感语句中重要的语义，提高预测的准确度。
Abstract: Sentiment analysis has many important applications in public opinion monitoring and business. In this paper, microblog comment text is taken as the research object. Word segmentation is carried out on microblog comment text, stopping words are removed, and Word2vec is used for word vector training to obtain the word vector. Attention mechanism is introduced in Bi-LSTM, and the results of Bi-LSTM two-way processing are weighted and output. Experiments show that Attention-Bi-LSTM can effectively identify the important semantics of emotional statements and improve the accuracy of prediction compared with Bi-LSTM.
文章引用：王彬, 蒋鸿玲, 吴槟. 基于Attention-Bi-LSTM的微博评论情感分析研究[J]. 计算机科学与应用, 2020, 10(12): 2380-2387. https://doi.org/10.12677/CSA.2020.1012252

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