@inproceedings{tabusca-etal-2026-spectrum,
title = "The Spectrum of Sentiment: Optimistic, Pessimistic, and Neutral Voices in Online Depression Discourse",
author = "Tabusca, Stefana Arina and
Bucur, Ana-Maria and
Dinu, Liviu P.",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.858/",
doi = "10.63317/3eqsahrxy75e",
pages = "10970--10981",
abstract = "The relationship between depression and the concepts of optimism and pessimism has been extensively researched by psychologists. In this paper, we use computational approaches to study how optimism and pessimism are expressed in the online discourse of people with a depression diagnosis. Publicly available datasets are used for the development of an optimism/pessimism detection model, as well as for the analyses performed on social media posts of individuals with depression, as measured by BDI-II, a validated depression questionnaire. To analyze the optimistic and pessimistic posts by individuals with depression, we use LIWC features and perform topic modeling. We also investigate specific words driving mislabeling using SHAP. Our results show that while there may not be significant differences in the number of optimistic versus pessimistic posts between individuals in the depression and control groups, the content of the posts differs meaningfully, both in terms of linguistic features and approached topics."
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<abstract>The relationship between depression and the concepts of optimism and pessimism has been extensively researched by psychologists. In this paper, we use computational approaches to study how optimism and pessimism are expressed in the online discourse of people with a depression diagnosis. Publicly available datasets are used for the development of an optimism/pessimism detection model, as well as for the analyses performed on social media posts of individuals with depression, as measured by BDI-II, a validated depression questionnaire. To analyze the optimistic and pessimistic posts by individuals with depression, we use LIWC features and perform topic modeling. We also investigate specific words driving mislabeling using SHAP. Our results show that while there may not be significant differences in the number of optimistic versus pessimistic posts between individuals in the depression and control groups, the content of the posts differs meaningfully, both in terms of linguistic features and approached topics.</abstract>
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%0 Conference Proceedings
%T The Spectrum of Sentiment: Optimistic, Pessimistic, and Neutral Voices in Online Depression Discourse
%A Tabusca, Stefana Arina
%A Bucur, Ana-Maria
%A Dinu, Liviu P.
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F tabusca-etal-2026-spectrum
%X The relationship between depression and the concepts of optimism and pessimism has been extensively researched by psychologists. In this paper, we use computational approaches to study how optimism and pessimism are expressed in the online discourse of people with a depression diagnosis. Publicly available datasets are used for the development of an optimism/pessimism detection model, as well as for the analyses performed on social media posts of individuals with depression, as measured by BDI-II, a validated depression questionnaire. To analyze the optimistic and pessimistic posts by individuals with depression, we use LIWC features and perform topic modeling. We also investigate specific words driving mislabeling using SHAP. Our results show that while there may not be significant differences in the number of optimistic versus pessimistic posts between individuals in the depression and control groups, the content of the posts differs meaningfully, both in terms of linguistic features and approached topics.
%R 10.63317/3eqsahrxy75e
%U https://aclanthology.org/2026.lrec-1.858/
%U https://doi.org/10.63317/3eqsahrxy75e
%P 10970-10981
Markdown (Informal)
[The Spectrum of Sentiment: Optimistic, Pessimistic, and Neutral Voices in Online Depression Discourse](https://aclanthology.org/2026.lrec-1.858/) (Tabusca et al., LREC 2026)
ACL