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Enhanced sentiment learning using Twitter hashtags and smileys

Authors:

Dmitry Davidov,

Ari RappoportAuthors Info & Claims

COLING '10: Proceedings of the 23rd International Conference on Computational Linguistics: Posters

Pages 241 - 249

Published: 23 August 2010 Publication History

Abstract

Automated identification of diverse sentiment types can be beneficial for many NLP systems such as review summarization and public media analysis. In some of these systems there is an option of assigning a sentiment value to a single sentence or a very short text.

In this paper we propose a supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service. By utilizing 50 Twitter tags and 15 smileys as sentiment labels, this framework avoids the need for labor intensive manual annotation, allowing identification and classification of diverse sentiment types of short texts. We evaluate the contribution of different feature types for sentiment classification and show that our framework successfully identifies sentiment types of untagged sentences. The quality of the sentiment identification was also confirmed by human judges. We also explore dependencies and overlap between different sentiment types represented by smileys and Twitter hashtags.

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Cited By

Chen ZCao YYao HLu XPeng XMei HLiu X(2021)Emoji-powered Sentiment and Emotion Detection from Software Developers’ Communication DataACM Transactions on Software Engineering and Methodology10.1145/342430830:2(1-48)Online publication date: 27-Jan-2021
https://dl.acm.org/doi/10.1145/3424308
Al Bashaireh RZohdy MSabeeh V(2020)Twitter Data Collection and ExtractionProceedings of the 2020 the 4th International Conference on Information System and Data Mining10.1145/3404663.3404686(71-76)Online publication date: 15-May-2020
https://dl.acm.org/doi/10.1145/3404663.3404686
Javari AHe ZHuang ZJeetu RChen-Chuan Chang K(2020)Weakly Supervised Attention for Hashtag Recommendation using Graph DataProceedings of The Web Conference 202010.1145/3366423.3380182(1038-1048)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380182
Show More Cited By

Index Terms

Enhanced sentiment learning using Twitter hashtags and smileys

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Information & Contributors

Information

Published In

cover image DL Hosted proceedings

COLING '10: Proceedings of the 23rd International Conference on Computational Linguistics: Posters

August 2010

1588 pages

General Chair:
Aravind K. Joshi
University of Pennsylvania
,
Program Chairs:
Chu-Ren Huang
The Hong Kong Polytechnic University
,
Dan Jurafsky
Stanford University

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 23 August 2010

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Overall Acceptance Rate 1,537 of 1,537 submissions, 100%

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Cited By

Chen ZCao YYao HLu XPeng XMei HLiu X(2021)Emoji-powered Sentiment and Emotion Detection from Software Developers’ Communication DataACM Transactions on Software Engineering and Methodology10.1145/342430830:2(1-48)Online publication date: 27-Jan-2021
https://dl.acm.org/doi/10.1145/3424308
Al Bashaireh RZohdy MSabeeh V(2020)Twitter Data Collection and ExtractionProceedings of the 2020 the 4th International Conference on Information System and Data Mining10.1145/3404663.3404686(71-76)Online publication date: 15-May-2020
https://dl.acm.org/doi/10.1145/3404663.3404686
Javari AHe ZHuang ZJeetu RChen-Chuan Chang K(2020)Weakly Supervised Attention for Hashtag Recommendation using Graph DataProceedings of The Web Conference 202010.1145/3366423.3380182(1038-1048)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380182
Beseiso MElmousalami H(2020)Subword Attentive Model for Arabic Sentiment AnalysisACM Transactions on Asian and Low-Resource Language Information Processing10.1145/336001619:2(1-17)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3360016
Botchway RJibril AKwarteng MChovancova MOplatková ZCiccarone GKumar V(2019)A review of social media posts from UniCredit bank in EuropeProceedings of the 3rd International Conference on Business and Information Management10.1145/3361785.3361814(74-79)Online publication date: 12-Sep-2019
https://dl.acm.org/doi/10.1145/3361785.3361814
Mittal APatidar S(2019)Sentiment Analysis on Twitter DataProceedings of the 7th International Conference on Computer and Communications Management10.1145/3348445.3348466(91-95)Online publication date: 27-Jul-2019
https://dl.acm.org/doi/10.1145/3348445.3348466
Chen ZCao YLu XMei QLiu XDumas MPfahl DApel SRusso A(2019)SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineeringProceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3338906.3338977(841-852)Online publication date: 12-Aug-2019
https://dl.acm.org/doi/10.1145/3338906.3338977
Chen ZShen SHu ZLu XMei QLiu X(2019)Emoji-Powered Representation Learning for Cross-Lingual Sentiment ClassificationThe World Wide Web Conference10.1145/3308558.3313600(251-262)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313600
Zhang Y(2019)Language in Our Time: An Empirical Analysis of HashtagsThe World Wide Web Conference10.1145/3308558.3313480(2378-2389)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313480
Xue RHuang SLuo XJiang DGuo YPeng Y(2018)Semantic emotion-topic model in social media environmentJournal of Web Engineering10.5555/3370048.337005217:1-2(73-92)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.5555/3370048.3370052
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