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Using Hashtags to Capture Fine Emotion Categories from Tweets

Authors:

Saif M. Mohammad,

Svetlana KiritchenkoAuthors Info & Claims

Computational Intelligence, Volume 31, Issue 2

Pages 301 - 326

https://doi.org/10.1111/coin.12024

Published: 01 May 2015 Publication History

Abstract

Detecting emotions in microblogs and social media posts has applications for industry, health, and security. Statistical, supervised automatic methods for emotion detection rely on text that is labeled for emotions, but such data are rare and available for only a handful of basic emotions. In this article, we show that emotion-word hashtags are good manual labels of emotions in tweets. We also propose a method to generate a large lexicon of word-emotion associations from this emotion-labeled tweet corpus. This is the first lexicon with real-valued word-emotion association scores. We begin with experiments for six basic emotions and show that the hashtag annotations are consistent and match with the annotations of trained judges. We also show how the extracted tweet corpus and word-emotion associations can be used to improve emotion classification accuracy in a different nontweet domain.

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cover image Computational Intelligence

Computational Intelligence Volume 31, Issue 2

May 2015

184 pages

ISSN:0824-7935

EISSN:1467-8640

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Blackwell Publishers, Inc.

United States

John Wiley & Sons, Inc.

United States

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Published: 01 May 2015

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Mukta MAhmed JRaiaan MFahad NIslam MImtiaz NIslam MAli MAzam S(2024)Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic ModelingSN Computer Science10.1007/s42979-024-03055-15:6Online publication date: 16-Jul-2024
https://doi.org/10.1007/s42979-024-03055-1
Kastrati MKastrati ZShariq Imran ABiba M(2024)Leveraging distant supervision and deep learning for twitter sentiment and emotion classificationJournal of Intelligent Information Systems10.1007/s10844-024-00845-062:4(1045-1070)Online publication date: 1-Aug-2024
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Veltmeijer EGerritsen CHindriks K(2023)Automatic Emotion Recognition for Groups: A ReviewIEEE Transactions on Affective Computing10.1109/TAFFC.2021.306572614:1(89-107)Online publication date: 1-Jan-2023
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