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#Emotional tweets

Author:

Saif M. MohammadAuthors Info & Claims

SemEval '12: Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation

Pages 246 - 255

Published: 07 June 2012 Publication History

Abstract

Detecting emotions in microblogs and social media posts has applications for industry, health, and security. However, there exists no microblog corpus with instances labeled for emotions for developing supervised systems. In this paper, we describe how we created such a corpus from Twitter posts using emotion-word hashtags. We conduct experiments to show that the self-labeled hashtag annotations are consistent and match with the annotations of trained judges. We also show how the Twitter emotion corpus can be used to improve emotion classification accuracy in a different domain. Finally, we extract a word-emotion association lexicon from this Twitter corpus, and show that it leads to significantly better results than the manually crafted WordNet Affect lexicon in an emotion classification task.

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Cited By

Debnath AConlan O(2023)A Critical Analysis of EmpatheticDialogues as a Corpus for Empathetic EngagementProceedings of the 2nd Empathy-Centric Design Workshop10.1145/3588967.3588973(1-6)Online publication date: 23-Apr-2023
https://dl.acm.org/doi/10.1145/3588967.3588973
Alshahrani MSamothrakis SFasli MAuger AStützle T(2019)Identifying idealised vectors for emotion detection using CMA-ESProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3322057(157-158)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3322057
Purpura AMasiero CSilvello GAntonio Susto G(2019)Supervised Lexicon Extraction for Emotion ClassificationCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316700(1071-1078)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316700
Show More Cited By

#Emotional tweets
1. Hardware
  1. Power and energy
    1. Power estimation and optimization

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Information & Contributors

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SemEval '12: Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation

June 2012

758 pages

General Chair:
Eneko Agirre
University of the Basque Country

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 07 June 2012

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Overall Acceptance Rate 8 of 31 submissions, 26%

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Cited By

Debnath AConlan O(2023)A Critical Analysis of EmpatheticDialogues as a Corpus for Empathetic EngagementProceedings of the 2nd Empathy-Centric Design Workshop10.1145/3588967.3588973(1-6)Online publication date: 23-Apr-2023
https://dl.acm.org/doi/10.1145/3588967.3588973
Alshahrani MSamothrakis SFasli MAuger AStützle T(2019)Identifying idealised vectors for emotion detection using CMA-ESProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3322057(157-158)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3322057
Purpura AMasiero CSilvello GAntonio Susto G(2019)Supervised Lexicon Extraction for Emotion ClassificationCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316700(1071-1078)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316700
Illendula ASheth A(2019)Multimodal Emotion ClassificationCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316549(439-449)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316549
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
Zhang YFu JShe DZhang YWang SYang J(2018)Text emotion distribution learning via multi-task convolutional neural networkProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304409(4595-4601)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304222.3304409
Ranganathan JHedge NIrudayaraj ATzacheva ASingh A(2018)Automatic detection of emotions in Twitter dataProceedings of the Workshop on Opinion Mining, Summarization and Diversification10.1145/3301020.3303751(1-10)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.1145/3301020.3303751
Buitelaar PWood INegi SArcan MMcCrae JAbele ARobin CAndryushechkin VZiad HSagha HSchmitt MSchuller BSanchez-Rada JIglesias CNavarro CGiefer AHeise NMasucci VDanza FCaterino CSmrz PHradis MPovolny FKlimes MMatejka PTummarello G(2018)MixedEmotionsIEEE Transactions on Multimedia10.1109/TMM.2018.279828720:9(2454-2465)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1109/TMM.2018.2798287
Yun WKim DKim J(2017)Multi-categorical social media sentiment analysis of corporate eventsProceedings of the International Conference on Electronic Commerce10.1145/3154943.3154957(1-8)Online publication date: 17-Aug-2017
https://dl.acm.org/doi/10.1145/3154943.3154957
Kolokythas PKomninos AMarini LGarofalakis JVlachos VSavvas ISgouropoulou CPlagianakos VDouligeris CVoyiatzis ITampakas VSoultis G(2017)A lightweight algorithm for the emotional classification of crowdsourced venue reviewsProceedings of the 21st Pan-Hellenic Conference on Informatics10.1145/3139367.3139422(1-6)Online publication date: 28-Sep-2017
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