Analysing Emotions in Cancer Narratives : A Corpus-Driven Approach

Lal, Daisy Monika and Rayson, Paul and Payne, Sheila A. and Liu, Yufeng (2024) Analysing Emotions in Cancer Narratives : A Corpus-Driven Approach. In: Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024 :. ELRA and ICCL, Torino, Italia, pp. 73-83.

Text (2024.cl4health-1.9) - Published Version
Available under License Creative Commons Attribution.
Download (0B)

Text (2024.cl4health-1.9) - Published Version
Available under License Creative Commons Attribution.
Download (0B)

Text (2024.cl4health-1.9) - Published Version
Available under License Creative Commons Attribution.
Download (0B)

Text (2024.cl4health-1.9) - Published Version
Available under License Creative Commons Attribution.
Download (0B)

Text (2024.cl4health-1.9)
2024.cl4health-1.9.pdf - Published Version
Available under License Creative Commons Attribution.
Download (614kB)

Abstract

Cancer not only affects a patient's physical health, but it can also elicit a wide spectrum of intense emotions in patients, friends, and family members. People with cancer and their carers (family member, partner, or friend) are increasingly turning to the web for information and support. Despite the expansion of sentiment analysis in the context of social media and healthcare, there is relatively less research on patient narratives, which are longer, more complex texts, and difficult to assess. In this exploratory work, we examine how patients and carers express their feelings about various aspects of cancer (treatments and stages). The objective of this paper is to illustrate with examples the nature of language in the clinical domain, as well as the complexities of language when performing automatic sentiment and emotion analysis. We perform a linguistic analysis of a corpus of cancer narratives collected from Reddit. We examine the performance of five state-of-the-art models (T5, DistilBERT, Roberta, RobertaGo, and NRCLex) to see how well they match with human comparisons separated by linguistic and medical background. The corpus yielded several surprising results that could be useful to sentiment analysis NLP experts. The linguistic issues encountered were classified into four categories: statements expressing a variety of emotions, ambiguous or conflicting statements with contradictory emotions, statements requiring additional context, and statements in which sentiment and emotions can be inferred but are not explicitly mentioned.