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Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons

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Social Network Based Big Data Analysis and Applications

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Sentiment lexicons are an essential component on most state-of-the-art sentiment analysis methods. However, the terms included are usually restricted to verbs and adjectives because they (1) usually have similar meanings among different domains and (2) are the main indicators of subjectivity in the text. This can lead to a problem in the classification of short informal texts since sometimes the absence of these types of parts of speech does not mean an absence of sentiment. Therefore, our hypothesis states that knowledge of terms regarding certain events and respective sentiment (public opinion) can improve the task of sentiment analysis. Consequently, to complement traditional sentiment dictionaries, we present a system for lexicon expansion that extracts the most relevant terms from news and assesses their positive or negative score through Twitter. Preliminary results on a labelled dataset show that our complementary lexicons increase the performance of three state-of-the-art sentiment systems, therefore proving the effectiveness of our approach.

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Acknowledgements

This work is supported by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within project Reminds/UTAP-ICDT/EEI-CTP/0022/2014.

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Authors and Affiliations

  1. CRACS - INESC TEC & University of Porto, Porto, Portugal

    Nuno Guimarães & Álvaro Figueira

  2. LIAAD - INESC TEC & University of Porto, Porto, Portugal

    Luís Torgo

Authors
  1. Nuno Guimarães
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  2. Luís Torgo
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  3. Álvaro Figueira
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Correspondence to Nuno Guimarães .

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Editors and Affiliations

  1. Department of Computer Engineering, Firat University, Elazig, Turkey

    Mehmet Kaya

  2. Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

    Jalal Kawash

  3. American University of Sharjah, Sharjah, United Arab Emirates

    Suheil Khoury

  4. Tamkang University, Taipei, Taiwan

    Min-Yuh Day

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Guimarães, N., Torgo, L., Figueira, Á. (2018). Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons. In: Kaya, M., Kawash, J., Khoury, S., Day, MY. (eds) Social Network Based Big Data Analysis and Applications. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-78196-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-78196-9_1

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