融合位置信息的观点三元组情感分析模型.

There are two kinds of tasks in aspect-based sentiment analysis: one is extraction task, which aims to extract aspect words and opinion words from sentences. The other is the classification task, which aims to analyze the sentimental polarity. On the basis of these two complex tasks, aiming at the problem that the coupling between aspect words and opinion words is poor, which leads to the error of classification task, this paper proposed an opinion triplet sentiment analysis model (OTPM) integrating position information. The model used bi-directional long short-term memory network to obtain the text representation, then used the self attention mechanism to enhance the correlation between aspect words and opinion words, and then extracted the opinion triplet in the multi task framework. At the same time, it conducted weighted fusion of extracted representation and position information. Finally, the experiment used biaffine scorer to analyze the sentimental dependence between the weighted aspect words and opinion words, and then used stop-on-non-I algorithm to decode the triplet and output the triplet. This paper conducted a lot of experiments on Lap14, Rest14, Rest15, Rest16 dataset. The results show that the proposed model is superior to a series of baseline models. [ABSTRACT FROM AUTHOR]