Author(s):
1. Nur Zareen Zulkarnain:
Centre for Advanced Computing Technology (C-ACT), Fakulti Teknologi Maklumat Dan Komunikasi
2. Hailizah Basiron:
Centre for Advanced Computing Technology (C-ACT), Fakulti Teknologi Maklumat Dan Komunikasi
3. Norida Abdullah:
Centre for Technopreneurship Development (C-TeD), Pusat Bahasa Dan Pembangunan Insan,
Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.
Abstract:
In today's digital age, social media have become the most common channel for individuals to express their opinions and feelings. As common as this, the extensive usage of social media has also been associated with mental illnesses such as anxiety, suicidality and depression. The digital traces the individuals left provide insights into not just their daily life but also on their health and mental state. This allows for various prediction and preliminary diagnosis to be made. The advancement of research in the Natural Language Processing (NLP) field has allowed researchers to understand individuals based on texts they shared in their social media account. This paper reviews the techniques and methods used in detecting depression from social media texts where emphasis are being placed on the writing style and the word usage of the social media users. Writing styles and choices of words have been seen as a possible indicator in detecting depression from social media texts. Various methods and platforms have been adopted to investigate the effectiveness of detecting depression based on these two components. This paper discusses these methods and techniques as well as the areas where improvements can be made.
Page(s):
124-135
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 98, Issue: 1, Year: 2020
Keywords:
ontology
,
lexicon
,
Text Analysis
,
Depression Detection
References:
References are not available for this document.
Citations
Citations are not available for this document.