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A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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Information 2020, 11(2), 92; https://doi.org/10.3390/info11020092
Received: 3 January 2020 / Revised: 4 February 2020 / Accepted: 5 February 2020 / Published: 8 February 2020
(This article belongs to the Section Artificial Intelligence)
Twitter sentiment analysis is an effective tool for various Twitter-based analysis tasks. However, there is still no neural-network-based research which takes both the tweet-text information and user-connection information into account. To this end, we propose the Attentional-graph Neural Network based Twitter Sentiment Analyzer (AGN-TSA), a Twitter sentiment analyzer based on attentional-graph neural networks. AGN-TSA fuses the tweet-text information and the user-connection information through a three-layered neural structure, which includes a word-embedding layer, a user-embedding layer and an attentional graph network layer. For the training of AGN-TSA, dedicated loss functions are designed for the structural controllability of AGN-TSA network. Experiments based on real-world dataset concerning the 2016 presidential election of America exhibit that AGN-TSA is superior under multiple metrics over several prevailing methods, with a performance boost of over 5%. The empirical settings of parameters are given based on extensive rotation experiments.
Keywords: Twitter; sentiment analysis; tweet; user connection; graph neural network; attention network; neural network structure Twitter; sentiment analysis; tweet; user connection; graph neural network; attention network; neural network structure
MDPI and ACS Style

Wang, M.; Hu, G. A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network. Information 2020, 11, 92.

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