Sentiment analysis of tweets using text and graph multi-views learning
Sentiment analysis of tweets using text and graph multi-views learning
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.
Graph neural network, Multi-view learning, Sentiment analysis, Sequence learning model
2965-2985
Singh, Loitongbam Gyanendro
c1d8ea4f-7a54-4c78-8830-3c3064e26ae6
Singh, Sanasam Ranbir
d0cd551a-b51e-4de6-9474-81d7257caf52
May 2024
Singh, Loitongbam Gyanendro
c1d8ea4f-7a54-4c78-8830-3c3064e26ae6
Singh, Sanasam Ranbir
d0cd551a-b51e-4de6-9474-81d7257caf52
Singh, Loitongbam Gyanendro and Singh, Sanasam Ranbir
(2024)
Sentiment analysis of tweets using text and graph multi-views learning.
Knowledge and Information Systems, 66 (5), .
(doi:10.1007/s10115-023-02053-8).
Abstract
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.
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KAIS_open_access
- Accepted Manuscript
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s10115-023-02053-8
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Accepted/In Press date: 14 December 2023
Published date: May 2024
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Publisher Copyright:
© The Author(s) 2024.
Keywords:
Graph neural network, Multi-view learning, Sentiment analysis, Sequence learning model
Identifiers
Local EPrints ID: 486542
URI: http://eprints.soton.ac.uk/id/eprint/486542
ISSN: 0219-1377
PURE UUID: 353c5a25-93e1-43bf-beaa-d14dcd4a8572
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Date deposited: 25 Jan 2024 17:35
Last modified: 31 Oct 2024 17:44
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Contributors
Author:
Loitongbam Gyanendro Singh
Author:
Sanasam Ranbir Singh
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