Skip to main content

A Comparison of Web Services for Sentiment Analysis in Digital Mental Health Interventions

  • Conference paper
  • First Online:
  • 1755 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13315))

Abstract

The use of web services allows for an easy and cost-effective way to implementation natural language processing capabilities such as sentiment analysis in digital interventions such as those used in mental healthcare. To the best of our knowledge, the majority of studies to date focus on the use of sentiment analysis for the analysis of user reviews and social platforms. This study thus aims to explore the use of 18 currently available web services in the analysis of user submitted content from a digital mental health intervention. The web services are compared on the basis of their accuracy, precision, recall, f-measures and mean square error. Given the sensitive nature of user content from digital mental health interventions, we also explored how the various web services handled the data submitted to them for analysis. The results of the study provide other researchers with a better idea of the performance and suitability of the various web services for use in digital mental health interventions.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Hollis, C., et al.: Technological innovations in mental healthcare: harnessing the digital revolution. Br. J. Psychiatry 206(4), 263–265 (2015)

    Article  Google Scholar 

  2. Cuijpers, P., Riper, H., Andersson, G.: Internet-based treatment of depression. Curr. Opin. Psychol. 4, 131–135 (2015)

    Article  Google Scholar 

  3. Coppersmith, G., Hilland, C., Frieder, O., Leary, R.: Scalable mental health analysis in the clinical whitespace via natural language processing, pp. 393–396. IEEE (2017)

    Google Scholar 

  4. Gkotsis, G., et al.: Characterisation of mental health conditions in social media using informed deep learning. Sci. Rep. 7(1), 1–11 (2017)

    Google Scholar 

  5. Miller, E., Polson, D.: Apps, avatars, and robots: the future of mental healthcare. Issues Ment. Health Nurs. 40(3), 208–214 (2019)

    Article  Google Scholar 

  6. Renn, B.N., Hoeft, T.J., Lee, H.S., Bauer, A.M., Areán, P.A.: Preference for in-person psychotherapy versus digital psychotherapy options for depression: survey of adults in the US. NPJ Digit. Med. 2(1), 1–7 (2019)

    Article  Google Scholar 

  7. Reid, S.C., et al.: A mobile phone application for the assessment and management of youth mental health problems in primary care: a randomised controlled trial. BMC Fam. Pract. 12(1), 1–14 (2011)

    Article  Google Scholar 

  8. Qu, C., Sas, C., Roquet, C.D., Doherty, G.: Functionality of top-rated mobile apps for depression: systematic search and evaluation, JMIR Ment. Health 7(1), e15321 (2020)

    Google Scholar 

  9. D’alfonso, S., et al.: Artificial intelligence-assisted online social therapy for youth mental health. Front. Psychol. 8, 796 (2017)

    Article  Google Scholar 

  10. Birjali, M., Beni-Hssane, A., Erritali, M.: Machine learning and semantic sentiment analysis based algorithms for suicide sentiment prediction in social networks. Procedia Comput. Sci. 113, 65–72 (2017)

    Article  Google Scholar 

  11. Madhu, S.: An approach to analyze suicidal tendency in blogs and tweets using sentiment analysis. Int. J. Sci. Res. Comput. Sci. Eng. 6(4), 34–36 (2018)

    MathSciNet  Google Scholar 

  12. Le Glaz, A., et al.: Machine learning and natural language processing in mental health: systematic review. J. Med. Internet Res. 23(5), e15708 (2021)

    Google Scholar 

  13. Ho, A.H.Y., et al.: A novel narrative e-writing intervention for parents of children with chronic life-threatening illnesses: protocol for a pilot, open-label randomized controlled trial. JMIR Res. Protoc. 9(7), e17561 (2020)

    Google Scholar 

  14. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    Article  Google Scholar 

  15. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)

    Article  Google Scholar 

  16. Tang, H., Tan, S., Cheng, X.: A survey on sentiment detection of reviews. Expert Syst. Appl. 36(7), 10760–10773 (2009)

    Article  Google Scholar 

  17. Kolchyna, O., Souza, T.T., Treleaven, P., Aste, T.: Twitter sentiment analysis: lexicon method, machine learning method and their combination. arXiv preprint arXiv:1507.00955 (2015)

  18. Xianghua, F., Guo, L., Yanyan, G., Zhiqiang, W.: Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowl.-Based Syst. 37, 186–195 (2013)

    Article  Google Scholar 

  19. Hu, Y., Li, W.: Document sentiment classification by exploring description model of topical terms. Comput. Speech Lang. 25(2), 386–403 (2011)

    Article  Google Scholar 

  20. Maks, I., Vossen, P.: A lexicon model for deep sentiment analysis and opinion mining applications. Decis. Support Syst. 53(4), 680–688 (2012)

    Article  Google Scholar 

  21. Xu, T., Peng, Q., Cheng, Y.: Identifying the semantic orientation of terms using S-HAL for sentiment analysis. Knowl.-Based Syst. 35, 279–289 (2012)

    Article  Google Scholar 

  22. Hagenau, M., Liebmann, M., Neumann, D.: automated news reading: stock price prediction based on financial news using context-capturing features. Decis. Support Syst. 55(3), 685–697 (2013)

    Article  Google Scholar 

  23. Tov, W., Ng, K.L., Lin, H., Qiu, L.: Detecting well-being via computerized content analysis of brief diary entries. Psychol. Assess, 25(4), 1069 (2013)

    Google Scholar 

  24. Catania, F., Di Nardo, N., Garzotto, F., Occhiuto, D.: Emoty: an emotionally sensitive conversational agent for people with neurodevelopmental disorders (2019)

    Google Scholar 

  25. Basmmi, A.B.M.N., Abd Halim, S., Saadon, N.A.: Comparison of web services for sentiment analysis in social networking sites, p. 012063. IOP Publishing (2020)

    Google Scholar 

  26. Gao, S., Hao, J., Fu, Y.: The application and comparison of web services for sentiment analysis in tourism, pp. 1–6. IEEE (2015)

    Google Scholar 

  27. Pinto, H.L., Rocio, V.: Combining sentiment analysis scores to improve accuracy of polarity classification in MOOC posts. In: Moura Oliveira, P., Novais, P., Reis, L.P. (eds.) EPIA 2019. LNCS (LNAI), vol. 11804, pp. 35–46. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30241-2_4

    Chapter  Google Scholar 

  28. Serrano-Guerrero, J., Olivas, J.A., Romero, F.P., Herrera-Viedma, E.: Sentiment analysis: a review and comparative analysis of web services. Inf. Sci. 311, 18–38 (2015)

    Article  Google Scholar 

  29. Park, M., Cha, C., Cha, M.: Depressive moods of users portrayed in Twitter (2012)

    Google Scholar 

  30. Choi, D., Kim, P.: Sentiment analysis for tracking breaking events: a case study on twitter. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013. LNCS (LNAI), vol. 7803, pp. 285–294. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36543-0_30

    Chapter  Google Scholar 

  31. Sell, J., Farreras, I.G.: LIWC-ing at a century of introductory college textbooks: have the sentiments changed? Procedia Comput. Sci. 118, 108–112 (2017)

    Article  Google Scholar 

  32. Syah, T., Apriyanto, S., Nurhayaty, A.: Student’s prevailing, confidence, and drives: LIWC analysis on self-description text, pp. 295–299. Atlantis Press (2020

    Google Scholar 

  33. Annett, M., Kondrak, G.: A comparison of sentiment analysis techniques: polarizing movie blogs. In: Bergler, S. (ed.) AI 2008. LNCS (LNAI), vol. 5032, pp. 25–35. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68825-9_3

    Chapter  Google Scholar 

  34. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis, pp. 347–354 (2005)

    Google Scholar 

  35. Bermingham, A., Smeaton, A.F.: A study of inter-annotator agreement for opinion retrieval, pp. 784–785 (2009)

    Google Scholar 

  36. Snow, R., O’connor, B., Jurafsky, D., Ng, A.Y.: Cheap and fast–but is it good? Evaluating non-expert annotations for natural language tasks, pp. 254–263 (2008)

    Google Scholar 

  37. Harada, S.: The roles of singapore standard english and singlish. Inf. Res. 40, 70–82 (2009)

    Google Scholar 

  38. Althnian, A., et al.: Impact of dataset size on classification performance: an empirical evaluation in the medical domain. Appl. Sci. 11(2), 796 (2021)

    Google Scholar 

Download references

Acknowledgements

This research is supported in part by the Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University, Singapore.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Toh Hsiang Benny Tan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tan, T.H.B., Lim, S., Qiu, Y., Miao, C. (2022). A Comparison of Web Services for Sentiment Analysis in Digital Mental Health Interventions. In: Meiselwitz, G. (eds) Social Computing and Social Media: Design, User Experience and Impact. HCII 2022. Lecture Notes in Computer Science, vol 13315. Springer, Cham. https://doi.org/10.1007/978-3-031-05061-9_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-05061-9_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05060-2

  • Online ISBN: 978-3-031-05061-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics