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

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Social Computing and Social Media: Design, User Experience and Impact (HCII 2022)

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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.

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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.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Toh Hsiang Benny Tan, Yang Qiu & Chunyan Miao

  2. Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore, Singapore

    Sufang Lim, Yang Qiu & Chunyan Miao

Authors
  1. Toh Hsiang Benny Tan
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  2. Sufang Lim
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Correspondence to Toh Hsiang Benny Tan .

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  1. Towson University, Towson, MD, USA

    Gabriele Meiselwitz

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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

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

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  • Online ISBN: 978-3-031-05061-9

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