Depression detection for twitter users using sentiment analysis in English and Arabic tweets.

Autor: Helmy A; Department of Information Systems and Technology Faculty of Graduate Studies for Statistical Research, Cairo University, Egypt. Electronic address: abdelmoniem.hafez@cu.edu.eg., Nassar R; Department of Information Systems and Technology Faculty of Graduate Studies for Statistical Research, Cairo University, Egypt., Ramdan N; Department of Information Systems and Technology Faculty of Graduate Studies for Statistical Research, Cairo University, Egypt.
Jazyk: angličtina
Zdroj: Artificial intelligence in medicine [Artif Intell Med] 2024 Jan; Vol. 147, pp. 102716. Date of Electronic Publication: 2023 Nov 19.
DOI: 10.1016/j.artmed.2023.102716
Abstrakt: Since depression often results in suicidal thoughts and leaves a person severely disabled daily, there is an elevated risk of premature mortality due to mental problems caused by depression. Therefore, it's crucial to identify the patient's mental illness as soon as possible. People are increasingly using social media platforms to express their opinions and share daily activities, which makes online platforms rich sources of early depression detection. The contribution of this paper is multifold. First, it presents five machine-learning models for Arabic and English depression detection using Twitter text. The best model for Arabic text achieved an f1-score of 96.6 % for binary classification to depressed and Non_dep. For English text without negation, the model achieved 92 % for binary classification and 88 % for multi-classification (depressed, indifferent, happy). For English text with negation, an 87 %, and 85 % f1 score was achieved for binary and multi-classification respectively. Second, the work introduced a manually annotated Arabic_Dep_tweets_10,000 corpus of 10.000 Arabic tweets, which covered neutral tweets as well as a variety of depressed and happy terms. In addition, two automatically annotated English corpora, Eng_without_negation_60.000 corpus of 60,172 English tweets and Eng_with_negation_57.000 corpus of 57,392 English tweets. Both covered a wide range of depressed and cheerful terms; however, Negation was included in the Eng_with_negation_57.000 corpus. Finally, this paper exposes a depression-detection web application which implements our optimal models to detect tweets that contain depression symptoms and predict depression trends for a person either using English or Arabic language.
Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2023. Published by Elsevier B.V.)
Databáze: MEDLINE
Externí odkaz: