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Signals of Public Opinion in Online Communication

Author

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  • Sandra González-Bailón
  • Georgios Paltoglou

Abstract

This study offers a systematic comparison of automated content analysis tools. The ability of different lexicons to correctly identify affective tone (e.g., positive vs. negative) is assessed in different social media environments. Our comparisons examine the reliability and validity of publicly available, off-the-shelf classifiers. We use datasets from a range of online sources that vary in the diversity and formality of the language used, and we apply different classifiers to extract information about the affective tone in these datasets. We first measure agreement (reliability test) and then compare their classifications with the benchmark of human coding (validity test). Our analyses show that validity and reliability vary with the formality and diversity of the text; we also show that ready-to-use methods leave much space for improvement when analyzing domain-specific content and that a machine-learning approach offers more accurate predictions across communication domains.

Suggested Citation

  • Sandra González-Bailón & Georgios Paltoglou, 2015. "Signals of Public Opinion in Online Communication," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 95-107, May.
    • Handle: RePEc:sae:anname:v:659:y:2015:i:1:p:95-107
    DOI: 10.1177/0002716215569192
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    References listed on IDEAS

    as
    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    2. Mike Thelwall & Pardeep Sud & Farida Vis, 2012. "Commenting on YouTube videos: From guatemalan rock to El Big Bang," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(3), pages 616-629, March.
    3. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2011. "Sentiment in Twitter events," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 406-418, February.
    4. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2011. "Sentiment in Twitter events," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 406-418, February.
    5. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    6. Mike Thelwall & Pardeep Sud & Farida Vis, 2012. "Commenting on YouTube videos: From guatemalan rock to El Big Bang," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(3), pages 616-629, March.
    Full references (including those not matched with items on IDEAS)

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