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Conference Papers Year : 2012

User-Supplied Sentiments in Tweets

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Abstract

Microblogging has become very popular among web users: Twitter broadcasted 200 millions of them every day in 2011. These tweets can be used for social studies, opinion mining, and sentiment analysis. Tweets have been effectively used to analyze opinions and sentiments from specific events such as TV political debates or conference presentations. To analyze tweets, researchers either use Natural Language Process- ing or human-analyses, e. g. with Amazon Mechanical Turks. In this paper we describe a third method that can replace or complement the first two: convincing the tweet authors to explicitly express their opinion using a simple unambiguous syntax--a tag--while they tweet. We report on our experience with the PolemicTweet system where authors tag the tweets they send during conferences and TV shows. We explain how we implemented PolemicTweet and discuss briefly the pro and cons. We believe that author-tagging technologies can effectively complement automated analysis, in particular for analyzing tweets that are by essence ambiguous, and could be ironic, sarcastic or cryptic. We describe the incentive we used to convince the authors and the result we obtained.
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Dates and versions

hal-00734407 , version 1 (21-09-2012)

Identifiers

  • HAL Id : hal-00734407 , version 1

Cite

Samuel Huron, Jean-Daniel Fekete. User-Supplied Sentiments in Tweets. IEEE Conference on Information Visualization (INFOVIS): 2nd Workshop on Interactive Visual Text Analysis, IEEE, Oct 2012, Seattle, Washington, USA, United States. ⟨hal-00734407⟩
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