Improved graph convolutional network for emotion analysis in social media text.

Bharti Khemani, Shruti Patil, Sachin Malave, Jaya Gupta

Abstract read

In one paragraph

Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

RETRACTED: HGLER: A hierarchical heterogeneous graph networks for enhanced multimodal emotion recognition in conversations.PloS one · 2025
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5 · Who and what money

Authors and funding

4 authors.

Bharti KhemaniA. P. Shah Institute of Technology, Mumbai University, Thane, India.

Shruti PatilSymbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, India.

Sachin MalaveHOD, Department of Computer Engineering, A. P. Shah Institute of Technology, Mumbai, India.

Jaya GuptaHOD, AIML Department, A. P. Shah Institute of Technology, Mumbai University, Mumbai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding emotions in social media text is crucial for mental health monitoring, sentiment analysis, and misinformation detection applications. This study presents an Improved Graph Convolutional Network (IGCN) that leverages the network structure of social media text to enhance emotion classification. Unlike conventional GCN models, our approach integrates a Pointwise Mutual Information (PMI) based graph construction method, which improves the representation of semantic relationships between words. Additionally, an attention mechanism selectively emphasizes contextually significant words, enhancing interpretability and classification accuracy.

Indexed as

Emotion AnalysisGraph-Based Sentiment ClassificationImproved Graph Convolutional Network (IGCN)Scalable natural language processing (NLP)Text graph convolutional networks (TextGCN)

Identifiers

PMID40491513

DOI10.1016/j.mex.2025.103325

PMCPMC12148580

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.