Text Sentiment Classification Model based on Fusion of DualChannel Features of CNN and BiLSTM
Authors: 
Meng Zeng, Zhonglin ZhangAuthors Info & Claims
Pages 110 - 113
Abstract
Text sentiment analysis is an important task in natural language processing (NLP), which aims to determine people's emotional tendency towards a certain topic or event by analyzing the language and emotion in the text. Aiming at the traditional emotion classification model can't fully capture the semantic information implied in short text comments, a two-channel emotion classification model based on CNN and BiLSTMl is proposed.Dynamic allocation weights introduced since the attention mechanism, build fusion BiLSTM and CNN's dual channel neural network architecture, and extract the bureau of emotional characteristics and emotional characteristics as global pay attention to the input feature fusion layer, through your emotions full text feature fusion module integration characteristic information and emotional polarity to break.Compared to the experimental results show that the model of emotion classification performance of the optimum Transformer model, this model (CNN-BiLSTM-AFF) on a public data set senti_weibo_100k accuracy, F1 value, the recall rate of 1.034%, 1.265% and 1.045% respectively.
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- Text Sentiment Classification Model based on Fusion of DualChannel Features of CNN and BiLSTM
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Graphical abstractDisplay Omitted
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Published In
September 2023
227 pages
ISBN:9798400707629
DOI:10.1145/3625403
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 November 2023
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ADMIT 2023
ADMIT 2023: 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology
September 15 - 17, 2023
Chengdu, China
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