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Keyword-Driven Depressive Tendency Model for Social Media Posts

  • Hsiao-Wei HuEmail author
  • Kai-Shyang Hsu
  • Connie LeeEmail author
  • Hung-Lin Hu
  • Cheng-Yen Hsu
  • Wen-Han Yang
  • Ling-yun Wang
  • Ting-An Chen
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 354)

Abstract

People are increasingly sharing posts on social media (e.g., Facebook, Twitter, Instagram) that include references to their moods/feelings pertaining to their daily lives. In this study, we used sentiment analysis to explore social media messages for hidden indicators of depression. In cooperation with domain experts, we defined a tendency towards depression as evidenced in social media messages based on DSM-5, a standard classification of mental disorders widely used in the U.S. We also developed three data engineering procedures for the extraction of keywords from posts presenting a depressive tendency. Finally, we created a keyword-driven depressive tendency model by which to detect indications of depression in posts on a major social media platform in Taiwan (PTT). The performance of the proposed model was evaluated using three keyword extraction procedures. The DSM-5-based procedure with manual filtering resulted in the highest accuracy (0.74).

Keywords

Big data Social media Depression NLP Sentiment analysis 

References

  1. 1.
  2. 2.
    The World Health Organization: Depression (2018). http://www.who.int/news-room/fact-sheets/detail/depression
  3. 3.
    John Tung Foundation: Taiwanese depression self-diagnose Scale (2004). https://www.jtf.org.tw/overblue/taiwan1/
  4. 4.
    John Tung Foundation: A Survey of the Correlation between College Students’ Life Stress and Depresstion Tendency (2005). https://www.jtf.org.tw/psyche/melancholia/survey.asp?This=66&Page=1
  5. 5.
    Yadollahi, A., Shahraki, A.G., Zaiane, O.R.: Current state of text sentiment analysis from opinion to emotion mining. ACM Comput. Surv. (CSUR) 50(2), 25 (2017)CrossRefGoogle Scholar
  6. 6.
    Strapparava, C., Valitutti, A.: Wordnet affect: an affective extension of wordnet. In: Lrec, vol. 4, pp. 1083–1086 (2004)Google Scholar
  7. 7.
    Pennebaker, J.W., Booth, R.J., Francis, M.E.: LIWC2007: linguistic inquiry and word count, Austin, Texas (2007). liwc.net
  8. 8.
    Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: using Mechanical Turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34. Association for Computational Linguistics, June, 2010Google Scholar
  9. 9.
    Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets (2013). arXiv preprint arXiv:1308.6242
  10. 10.
    Li, W., Xu, H.: Text-based emotion classification using emotion cause extraction. Expert Syst. Appl. 41(4), 1742–1749 (2014)CrossRefGoogle Scholar
  11. 11.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). arXiv preprint arXiv:1301.3781
  12. 12.
    word2vec, Google Code Archive (2013). https://code.google.com/archive/p/word2vec/

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hsiao-Wei Hu
    • 1
    Email author
  • Kai-Shyang Hsu
    • 1
  • Connie Lee
    • 1
    Email author
  • Hung-Lin Hu
    • 1
  • Cheng-Yen Hsu
    • 1
  • Wen-Han Yang
    • 1
  • Ling-yun Wang
    • 1
  • Ting-An Chen
    • 1
  1. 1.School of Big Data ManagementSoochow UniversityTaipeiTaiwan

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