CoSFGCN: Co-Sensitive Fusion Graph Convolution Network for Sentiment Analysis

CoSFGCN: Co-Sensitive Fusion Graph Convolution Network for Sentiment Analysis
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© 2023 by IJETT Journal
Volume-71 Issue-10
Year of Publication : 2023
Author : M.Priya Alagu Dharshini, S. Antelin Vijila, S.P. Victor
DOI : 10.14445/22315381/IJETT-V71I10P228

How to Cite?

M.Priya Alagu Dharshini, S. Antelin Vijila, S.P. Victor, "CoSFGCN: Co-Sensitive Fusion Graph Convolution Network for Sentiment Analysis," International Journal of Engineering Trends and Technology, vol. 71, no. 10, pp. 314-325, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I10P228

Abstract
Review analysis greatly influences the business industry to a greater level because it expresses the notion of customers. Many researchers addressed this issue by classifying the sentiments with better compositionality. The main objective of this research work is to handle the issues of excessive content, language imperfections, and less-intensive emotional words which affect the performances of sentiment analysis. This paper proposes a novel framework known as a Co-Sensitive Fusion Graph Convolution Network (CoSFGCN), which is based on a Graph Convolutional Network (GCN). This framework incorporates the properties of Syntactic and Co-Sensitive Specific Semantic GCN (〖CS〗^3 GCN) to utilise both the semantics and syntax of the words to provide additional weightage for graph learning. The performance analysis done on five benchmark datasets, LAP14, TWITTER_15, REST16, REST15, and REST14, shows better results when compared with the previous methods.

Keywords
CoSFGCN, GCN, Graph learning, Semantic graph model, Sentiment analysis.

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