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Topic‐based sentiment analysis for the social web: The role of mood and issue‐related words

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  • Mike Thelwall
  • Kevan Buckley

Abstract

General sentiment analysis for the social web has become increasingly useful for shedding light on the role of emotion in online communication and offline events in both academic research and data journalism. Nevertheless, existing general‐purpose social web sentiment analysis algorithms may not be optimal for texts focussed around specific topics. This article introduces 2 new methods, mood setting and lexicon extension, to improve the accuracy of topic‐specific lexical sentiment strength detection for the social web. Mood setting allows the topic mood to determine the default polarity for ostensibly neutral expressive text. Topic‐specific lexicon extension involves adding topic‐specific words to the default general sentiment lexicon. Experiments with 8 data sets show that both methods can improve sentiment analysis performance in corpora and are recommended when the topic focus is tightest.

Suggested Citation

  • Mike Thelwall & Kevan Buckley, 2013. "Topic‐based sentiment analysis for the social web: The role of mood and issue‐related words," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(8), pages 1608-1617, August.
    • Handle: RePEc:bla:jamist:v:64:y:2013:i:8:p:1608-1617
    DOI: 10.1002/asi.22872
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    Cited by:

    1. Hajar Sotudeh & Zeinab Saber & Farzin Ghanbari Aloni & Mahdieh Mirzabeigi & Farshad Khunjush, 2022. "A longitudinal study of the evolution of opinions about open access and its main features: a twitter sentiment analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5587-5611, October.
    2. Pal Singh, Satender & Adhikari, Arnab & Majumdar, Adrija & Bisi, Arnab, 2022. "Does service quality influence operational and financial performance of third party logistics service providers? A mixed multi criteria decision making -text mining-based investigation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    3. Gang Wang & Daqing Zheng & Shanlin Yang & Jian Ma, 2018. "FCE-SVM: a new cluster based ensemble method for opinion mining from social media," Information Systems and e-Business Management, Springer, vol. 16(4), pages 721-742, November.

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