A new emotion analysis fusion and complementary model based on online food reviews. (March 2022)

• Record Type: Journal Article
• Authors: Yong, Li; Xiaojun, Yang; Yi, Liu; Ruijun, Liu; Qingyu, Jin
• Is Part Of: Computers & electrical engineering. Volume 98(2022)
• Publication Date: 2022-03
• Subjects: Emotion analysis -- CNN -- BLSTM -- Bert -- CRF -- Deep learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107679
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: As the content of the food Review text becomes more and more complex and the types of the food Review text representation become more and more diverse, emotion analysis becomes more and more important in dealing with problems. At present are faced with the problem, this paper puts forward a new type of complementary fusion model, the text pretreatment by Bert training text model, make the content of the food review text more coherent, keep on convolutional neural network logic timing relationships, before and after the statement of text vector of the local feature extraction, using the method of bidirectional long short-term memory network (BLSTM) capture and text context information related to global long-range dependence, using the weighted formula of attention mechanism to improve the comment text statement attention emotional words and then with the weight of characteristic value after conditions with the airport of sorting the output. Through a comparative experiment on the data set of JD food review data set reviews, the results show that BCBLAC, the feature fusion model proposed in this paper, is much more accurate than the existing model in the food Review text language classification.