Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda

Domain:

healthcarenatural language processing

Record type:

paperdatasetmodel
Creator:
KimRimKirUdo
Host:avatar
Depression is a global burden and one of the most challenging mental health conditions to control. Experts can detect its severity early using the Beck Depression Inventory (BDI) questionnaire, administer appropriate medication to patients, and impede its progression. Due to the fear of potential stigmatization, many patients turn to social media platforms like Reddit for advice and assistance at various stages of their journey. This research extracts text from Reddit to facilitate the diagnostic process. It employs a proposed labeling approach to categorize the text and subsequently fine-tunes the Longformer model. The model's performance is compared against baseline models, including Naive Bayes, Random Forest, Support Vector Machines, and Gradient Boosting. Our findings reveal that the Longformer model outperforms the baseline models in both English (48%) and Luganda (45%) languages on a custom-made dataset. In IEEE Proceedings of the 14th International Conference on ICT Convergence (ICTC), Jeju, Korea, October 2023

Visit

arxiv.org

Tasks

text classification

Languages

Ganda

Tags

Computation and LanguageMachine Learning

Similar

Fine-Tuning BERT on Twitter and Reddit Data in Luganda and Englishaltaseb12/Automatic-Amharic-Text-News-Classification-Using-Fine--Tuned-BERT-ModelsDhati+: Fine-tuned Large Language Models for Arabic Subjectivity EvaluationFine-tuned models: Galician, Iban, SetswanaManziHilbert/Fined-tuned-NLLB-model-luganda-EnglishAn analysis of fine-tuned representations for code-switched speech recognition of Yorùbá and English

Fine-Tuning BERT on Twitter and Reddit Data in Luganda and English

altaseb12/Automatic-Amharic-Text-News-Classification-Using-Fine--Tuned-BERT-Models

Automatic Amharic Text News Classification Using Fine- Tuned BERT Models like XML-R,Afri berta

Dhati+: Fine-tuned Large Language Models for Arabic Subjectivity Evaluation

Despite its significance, Arabic, a linguistically rich and morphologically complex language, faces

Fine-tuned models: Galician, Iban, Setswana

wav2vec 2.0 XLSR-128 models (with and without adaptation via continued pre-training) fine-tuned on 1

ManziHilbert/Fined-tuned-NLLB-model-luganda-English

An analysis of fine-tuned representations for code-switched speech recognition of Yorùbá and English

An analysis of fine-tuned representations for code-switched speech recognition of Yorùbá and English

Poster presented at the Deep Learning Indaba 2022 by Tolúlọpẹ́ Ògúnrẹ̀mí