基于BERT-Attention-BiLSTM的多特征主题情感分析

Multi-feature topic sentiment analysis based on BERT-Attention-BiLSTM

关注微博用户对于事件的情感倾向,有利于平台了解用户心声,也能为决策者的舆情处理工作提供参考和方向。然而,当前大部分微博情感分析研究仍是基于文本的,忽略了表情、图片等要素。针对上述问题,本文提出了一个多模型融合的情感分析模型,以BERT预训练模型为基础,融合情感词典,并采用双向LSTM获取文本特征,有效联系前后文,并引入注意力机制,同时提出了一种emoji表情特征计算方法,得到一个情感分类更准确的多特征主题情感分析模型。

Currently,paying attention to Weibo users’emotional tendencies towards events will help the platform understand users’voices,and can also provide reference and direction for decision makers in handling public opinion.However,most of the current microblog sentiment analysis research is still based on text,ignoring elements such as expressions and pictures.In response to the above problems,this paper proposes a multi-model fusion sentiment analysis model,which integrates sentiment lexicon based on the BERT model,and uses two-way LSTM to obtain text features,effectively connects context,and introduces an attention mechanism.At the same time,a calculation method of emoji expression features is proposed,and a multi-feature topic sentiment analysis model with more accurate classification is proposed.