| Full text | |
| Author(s): |
Giuntini, Felipe T.
[1]
;
Cazzolato, Mirela T.
[1]
;
dos Reis, Maria de Jesus Dutra
[2]
;
Campbell, Andrew T.
[3]
;
Traina, Agma J. M.
[1]
;
Ueyama, Jo
[1]
Total Authors: 6
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[2] Univ Fed Sao Carlos, Dept Psychol, Sao Carlos, SP - Brazil
[3] Dartmouth Coll, Dept Comp Sci, Hanover, NH 03755 - USA
Total Affiliations: 3
|
| Document type: | Review article |
| Source: | JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING; v. 11, n. 11, SI, p. 4713-4729, NOV 2020. |
| Web of Science Citations: | 7 |
| Abstract | |
Social networks have become another resource for supporting mental health specialists in making inferences and finding indications of mental disorders, such as depression. This paper addresses the state-of-the-art regarding studies on recognition of depressive mood disorders in social networks through approaches and techniques of sentiment and emotion analysis. The systematic research conducted focused on social networks, social media, and the most employed techniques, feelings, and emotions were analyzed to find predecessors of a depressive disorder. Discussions on the research gaps identified aimed at improving the effectiveness of the analysis process, bringing the analysis close to the user's reality. Twitter, Facebook, Blogs and Forums, Reddit, Live Journal, and Instagram are the most employed social networks regarding the identification of depressive mood disorders, and the most used information wastext, followed by emoticons, user log information, and images. The selected studies usually employ classic off-the-shelf classifiers for the analysis of the available information, combined with lexicons such as NRC Word-Emoticon Association Lexicon, WordNet-Affect, Anew, and LIWC tool. The challenges include the analysis of temporal information and a combination of different types of information. (AU) | |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/17335-9 - Exploring DLTs and Computational Intelligence in IoT |
| Grantee: | Jó Ueyama |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 18/24414-2 - A framework for integration of feature extraction techniques and complex databases for MIVisBD |
| Grantee: | Mirela Teixeira Cazzolato |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |