Microblog Text Emotion Classification Algorithm Based on TCN-BiGRU and Dual Attention

Microblog is an important platform for mining public opinion, and it is of great value to conduct emotional analysis of microblog texts during the current epidemic. Aiming at the problem that most current emotional classification methods cannot effectively extract deep text features, and that tradit...

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Bibliographic Details
Published in:Information (Basel) 2023-02, Vol.14 (2), p.90
Main Authors: Qin, Yao, Shi, Yiping, Hao, Xinze, Liu, Jin
Format: Article
Language:English
Subjects:
ALBERT
Algorithms
Analysis
China
Classification
Coronaviruses
COVID-19
Data mining
Deep learning
Dictionaries
dual attention
Emotions
Epidemics
Feature extraction
fusion features
Machine learning
microblog text
Microblogs
Neural networks
Public health
Public opinion
Semantics
Sentiment analysis
sentiment classification
Social networks
TCN-BiGRU
Time series
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Summary:Microblog is an important platform for mining public opinion, and it is of great value to conduct emotional analysis of microblog texts during the current epidemic. Aiming at the problem that most current emotional classification methods cannot effectively extract deep text features, and that traditional word vectors cannot dynamically obtain the semantics of words according to their context, which leads to classification bias, this research put forward a microblog text emotion classification algorithm based on TCN-BiGRU and dual attention (TCN-BiGRU-DATT). First, the vector representation of the text was obtained using ALBERT. Second, the TCN and BiGRU networks were used to extract the emotional information contained in the text through dual pathway feature extraction, to efficiently obtain the deep semantic features of the text. Then, the dual attention mechanism was introduced to allocate the global weight of the key information in the semantic features, and the emotional features were spliced and fused. Finally, the Softmax classifier was applied for emotion classification. The findings of a comparative experiment on a set of microblog text comments collected throughout the pandemic revealed that the accuracy, recall, and F1 value of the emotion classification method proposed in this paper reached 92.33%, 91.78%, and 91.52%, respectively, which was a significant improvement compared with other models.
ISSN:2078-2489
2078-2489
DOI:10.3390/info14020090