Be conscientious, express your sentiment!

Fabio Celli, Cristina Zaga

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Abstract

This paper addresses the issue of how personality recognition can be helpful for sentiment analysis. We exploited the corpus for sentiment analysis released for the SEMEVAL 2013, we automatically annotated personality labels by means of an unsupervised system for personality recognition. We validated the automatic annotation on a small set of Twitter users, whose personality types have been collected by means of an online test. Results show that hashtag position and conscientiousness are the best predictors of sentiment in Twitter.

Original languageEnglish
Title of host publicationESSEM 2013: Emotion and Sentiment in Social and Expressive Media
Subtitle of host publicationProceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM 2013)
EditorsCristina Battaglino, Cristina Bosco, Erik Cambria, Rossana Damiano, Viviana Patti, Paolo Rosso
Place of PublicationAachen
PublisherCEUR
Pages140-147
Number of pages8
Publication statusPublished - 2013
Externally publishedYes
Event1st International Workshop on Emotion and Sentiment in Social and Expressive Media, ESSEM 2013 - Turin, Italy
Duration: 3 Dec 20133 Dec 2013
Conference number: 1

Publication series

NameCEUR Workshop Proceedings
Publisher1096
Volume1096
ISSN (Print)1613-0073

Conference

Conference1st International Workshop on Emotion and Sentiment in Social and Expressive Media, ESSEM 2013
Abbreviated titleESSEM 2013
CountryItaly
CityTurin
Period3/12/133/12/13

Keywords

  • Data mining
  • Personality recognition
  • Sentiment analysis
  • Twitter

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