Improved BLSTM Food Review Sentiment Analysis with Positional Attention Mechanisms
[1]Li Yong,Jin Qingyu,Zhang Qingchuan.Improved BLSTM Food Review Sentiment Analysis with Positional Attention Mechanisms[J].Journal of Zhengzhou University (Engineering Science),2020,41(01):58-62.[doi:10.13705/j.issn.1671-6833.2020.01.006]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41
Number of periods:
2020 01
Page number:
58-62
Column:
Public date:
2020-03-10
- Title:
- Improved BLSTM Food Review Sentiment Analysis with Positional Attention Mechanisms
- CLC:
- -
- Abstract:
- Sentiment analysis is a research hotspot in the field of natural language processing in recent years. However, the current deep learning model lacks the importance of studying the position of emotional words for the whole sentiment analysis in the emotional analysis of text sentences. In the sentiment semantic analysis of e-commerce commodity review data, CNN has certain advantages in extracting the structural features of the target, and can extract a variety of local features. RNN has memory function and has certain advantages in sequence feature extraction. Bidirectional Long Short-Term Memory (BLSTM) can achieve good results in extracting remote-dependent sequence semantic features. Based on BLSTM, this paper introduces a positional attention mechanism based on the combination of semantic role labeling and location in the food field. The distance-related sequence semantic feature extraction is realized, and the sentiment semantic classification of sequence semantic features is realized by CNN, and a food comment sentiment analysis model based on BLSTM and positional attention mechanism is constructed. Experimental results show that the model designed in this paper It has achieved good results in emotional classification, and has improved the accuracy rate results compared with the previous sentiment classification model
- References:
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Last Update:
2020-02-22
