research-article

Sentiment Analysis using Word-Graphs

Authors:

Konstantinos Tserpes,

Evangelos Psomakelis,

Konstantinos Psychas,

Theodora VarvarigouAuthors Info & Claims

WIMS '16: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

Article No.: 22, Pages 1 - 9

https://doi.org/10.1145/2912845.2912863

Published: 13 June 2016 Publication History

Abstract

The Word-Graph Sentiment Analysis Method is proposed to identify the sentiment that expressed in a microblog document using the sequence of the words that contains. The sequence of the words can be represented using graphs in which graph similarity metrics and classification algorithms can be applied to produce sentiment predictions. Experiments that were carried out with this method in a Twitter dataset validate the proposed model and allow us to further understand the metrics and the criteria that can be applied in words-graphs to predict the sentiment disposition of short, microblog documents.

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Cited By

Tiwari DNagpal BBhati BMishra AKumar M(2023)A systematic review of social network sentiment analysis with comparative study of ensemble-based techniquesArtificial Intelligence Review10.1007/s10462-023-10472-w56:11(13407-13461)Online publication date: 12-Apr-2023
https://doi.org/10.1007/s10462-023-10472-w
Phan HNguyen NMazur ZHwang D(2022)Sentence-level Sentiment Analysis Using GCN on Contextualized Word RepresentationsComputational Science – ICCS 202210.1007/978-3-031-08754-7_71(690-702)Online publication date: 15-Jun-2022
https://doi.org/10.1007/978-3-031-08754-7_71
Lovera FCardinale YHomsi M(2021)Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning ClassificationElectronics10.3390/electronics1022273910:22(2739)Online publication date: 10-Nov-2021
https://doi.org/10.3390/electronics10222739
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WIMS '16: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

June 2016

309 pages

ISBN:9781450340564

DOI:10.1145/2912845

Copyright © 2016 ACM.

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Published: 13 June 2016

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WIMS '16

WIMS '16: International Conference on Web Intelligence, Mining and Semantics

June 13 - 15, 2016

Nîmes, France

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WIMS '16 Paper Acceptance Rate 36 of 53 submissions, 68%;

Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

Tiwari DNagpal BBhati BMishra AKumar M(2023)A systematic review of social network sentiment analysis with comparative study of ensemble-based techniquesArtificial Intelligence Review10.1007/s10462-023-10472-w56:11(13407-13461)Online publication date: 12-Apr-2023
https://doi.org/10.1007/s10462-023-10472-w
Phan HNguyen NMazur ZHwang D(2022)Sentence-level Sentiment Analysis Using GCN on Contextualized Word RepresentationsComputational Science – ICCS 202210.1007/978-3-031-08754-7_71(690-702)Online publication date: 15-Jun-2022
https://doi.org/10.1007/978-3-031-08754-7_71
Lovera FCardinale YHomsi M(2021)Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning ClassificationElectronics10.3390/electronics1022273910:22(2739)Online publication date: 10-Nov-2021
https://doi.org/10.3390/electronics10222739
Zhao XLiu YJin Z(2021)Multiplex network embedding for implicit sentiment analysisComplex & Intelligent Systems10.1007/s40747-021-00504-9Online publication date: 6-Sep-2021
https://doi.org/10.1007/s40747-021-00504-9

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