中圖分類號(hào)： TP391.41
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.200559
中文引用格式： 楊嘉佳，熊仁都，劉金，等. 基于特征級(jí)注意力的方面級(jí)情感分類模型研究[J].電子技術(shù)應(yīng)用，2021，47(7)：78-82.
英文引用格式： Yang Jiajia，Xiong Rendou，Liu Jin，et al. Research on aspect level sentiment classification model based on feature level attention[J]. Application of Electronic Technique，2021，47(7)：78-82.

Research on aspect level sentiment classification model based on feature level attention

Abstract： In recent years, big data, natural language processing and other technologies have been developed rapidly. As one of the cutting-edge technologies in the field of natural language processing, emotion analysis has received great attention. However, High precision and high performance are still the key factors restricting emotional analysis. In order to achieve high-precision emotion analysis, based on the feature-level neural network, this paper improves the reset feature level attention mechanism, and proposes an aspect level emotion classification model based on the reset feature level attention(RFWA) and an aspect level emotion classification model based on the reset feature level self-attention(RFWSA). Finally, combined with Bi-LSTM-CRF,high quality aspect level emotion analysis is realized by aspect level phrase extraction in the network. The experimental results show that compared with the existing mainstream emotion analysis model, the model proposed in this paper has obvious advantages. Especially when the classification effect is quite good, the parameters of the model are only 1/4 of the AOA Network.

Key words : emotion analysis；aspect level；feature level；self attention