TechRxiv
IEEE_Journal_Paper_Template.pdf (726.67 kB)
Download file

Sentiment Analysis of Tweets using Text and Graph Multi-views learning

Download (726.67 kB)
preprint
posted on 27.04.2022, 03:35 by Loitongbam Gyanendro SinghLoitongbam Gyanendro Singh, Sanasam Ranbir Singh
With the surge of deep learning framework, various studies have attempted to address the challenges of sentiment analysis of tweets (data sparsity, under-specificity, noise, and multilingual content) through text and network-based representation learning approaches.
However, limited studies on combining the benefits of textual and structural (graph) representations for sentiment analysis of tweets have been carried out. This study proposes a multi-view learning framework (end-to-end and ensemble-based) that leverages both text-based and graph-based representation learning approaches to enrich the tweet representation for sentiment classification. The efficacy of the proposed framework is evaluated over three datasets using suitable baseline counterparts. From various experimental studies, it is observed that combining both textual and structural views can achieve better performance of sentiment classification tasks than its counterparts.

History

Email Address of Submitting Author

gyanendrol9@iitg.ac.in

ORCID of Submitting Author

0000-0002-7594-6146

Submitting Author's Institution

Indian Institute of Technology Guwahati

Submitting Author's Country

India

Usage metrics

Licence

Exports