基于深层注意力的LSTM的特定主题情感分析

In the current aspect sentiment analysis task, the traditional attention-based deep learning model lacks the effective attention to the aspect information and the sentiment information. This paper put forward a new LSTM model, which combining aspect information and deeper attention. Through the bidirectional LSTM with shared weights, it trained the aspect embedding and the text embedding to get the aspect feature and text feature to carry on the feature fusion, and after the deeper attention mechanism processing, it obtained the classification result of the corresponding aspect by the classifier. The experimental results of the SemEval-2014 Task4 and SemEval-2017 Task4 datasets show that this method has further improved the accuracy and stability of the attention-based sentiment analysis model in the aspect sentiment analysis. The introduction of aspect features and deeper attention mechanisms is of great significance to the task of sentiment analysis based on aspect, which provides method support for public opinion analysis, question answering system and text reasoning.