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8 June 2024 Short text sentiment analysis combining sentiment lexicon and graph convolutional networks
Peiyi Qu, Yonglin Leng
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Proceedings Volume 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024); 1317125 (2024) https://doi.org/10.1117/12.3032025
Event: 3rd International Conference on Algorithms, Microchips and Network Applications (AMNA 2024), 2024, Jinan, China
Abstract
In today's era of rapid development in information technology, short-text data has surged on various social networking platforms. How to quickly and accurately analyze people's emotional tendencies from these vast and complex data is a highly challenging task in the field of short-text data analysis. This paper proposes a short-text sentiment analysis framework that integrates a sentiment lexicon and graph convolutional neural networks (GCN). The framework utilizes the sentiment dictionary to enhance sentiment recognition and employs GCN to process complex data structures, learning the emotional features of short texts, and ultimately achieving short-text sentiment classification. To verify the effectiveness of the model, we conducted validation on public datasets. The experimental results show that this model significantly improves classification accuracy and recall rate compared to traditional single models.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Peiyi Qu and Yonglin Leng "Short text sentiment analysis combining sentiment lexicon and graph convolutional networks", Proc. SPIE 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 1317125 (8 June 2024); https://doi.org/10.1117/12.3032025
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KEYWORDS
Data modeling
Emotion
Analytical research
Matrices
Deep learning
Data mining
Performance modeling
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