Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (10): 100-105.DOI: 10.3778/j.issn.1002-8331.1905-0174

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Multi-attention Hierarchical Neural Network for Text Sentiment Analysis

HAN Hu, LIU Guoli, SUN Tianyue, ZHAO Qitao   

  1. 1.School of Electronic & Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
    2.Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic & Image Processing, Lanzhou 730070, China
  • Online:2020-05-15 Published:2020-05-13



  1. 1.兰州交通大学 电子与信息工程学院,兰州 730070
    2.甘肃省人工智能与图形图像工程研究中心,兰州 730070


Sentiment analysis aims to analyze people’s sentiments or opinions according to their generated texts, and plays a critical role in the area of data mining and natural language processing. Neural network methods have achieved promising results for text sentiment analysis. In the field of text sentiment analysis, document-level sentiment classification aims to predict user’s overall sentiment in a document about a product, and a review includes different granularity semantic information—word-, sentence- and document-level, and different words and sentences play different roles in sentiment classification. It seems too simple that the review is directly used to analyze the user sentiment, and the user information and the evaluated product information are also ignored. It is a common sense that the user’s preference and product’s characteristics make a significant influence on interpreting the sentiment of text. To address this issue, a hierarchical neural network model based on multi-attention mechanism is proposed in this paper. It obtains semantic information from word level, sentence level and document level respectively, and importance degrees calculated from sentence level and document level are combined by importing the user-and-product-attention mechanism. Experimental results on three real-world datasets show that the performance of this model is significantly improved compared with other models.

Key words: sentiment analysis, neural network, granularity, attention mechanism



关键词: 情感分析, 神经网络, 粒度, 注意力机制