AHRNN: Attention‐Based Hybrid Robust Neural Network for emotion recognition. Issue 1 (22nd February 2022)

• Record Type: Journal Article
• Authors: Xu, Ke; Liu, Bin; Tao, Jianhua; Lv, Zhao; Fan, Cunhang; Song, Leichao
• Is Part Of: Cognitive computation and systems. Volume 4:Issue 1(2022)
• Page Start: 85
• Page End: 95
• Publication Date: 2022-02-22
• Subjects: affective computing -- artificial intelligence -- artificial neural networks; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs2.12038
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract: In order to solve the problem that the existing methods cannot effectively capture the semantic emotion of the sentence when faced with the lack of cross‐language corpus, it is difficult to effectively perform cross‐language sentiment analysis, we propose a neural network architecture called the Attention‐Based Hybrid Robust Neural Network. The proposed architecture includes pre‐trained word embedding with fine‐tuning training to obtain prior semantic information, two sub‐networks and attention mechanism to capture the global semantic emotional information in the text, and a fully connected layer and softmax function to jointly perform final emotional classification. The Convolutional Neural Networks sub‐network captures the local semantic emotional information of the text, the BiLSTM sub‐network captures the contextual semantic emotional information of the text, and the attention mechanism dynamically integrates the semantic emotional information to obtain key emotional information. We conduct experiments on Chinese (International Conference on Natural Language Processing and Chinese Computing) and English (SST) datasets. The experiment is divided into three subtasks to evaluate the superiority of our method. It improves the recognition accuracy of single sentence positive/negative classification from 79% to 86% in the single‐language emotion recognition task. The recognition performance of fine‐grained emotional tags is also improved by 9.6%. The recognition accuracy of cross‐language emotion recognition tasks has also been improved by 1.5%. Even in the face of faulty data, the performance of our model is not significantly reduced when the error rate is less than 20%. These experimental results prove the superiority of our method.