The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review
Open accessAbstract
Sentiment analysis is an emerging trend nowadays to understand people’s sentiments in multiple situations in their quotidian life. Social media data would be utilized for the entire process ie the analysis and classification processes and it consists of text data and emoticons, emojis, etc. Many experiments were conducted in the antecedent studies utilizing Binary and Ternary Classification whereas Multi-class Classification gives more precise and precise Classification. In Multi-class Classification, the data would be divided into multiple sub-classes predicated on the polarities. Machine Learning and Deep Learning Techniques would be utilized for the classification process. Utilizing Social media, sentiment levels can be monitored or analysed. This paper shows a review of the sentiment analysis on Social media data for apprehensiveness or dejection detection utilizing various artificial intelligence techniques. In the survey, it was optically canvassed that social media data which consists of texts,emoticons and emojis were utilized for the sentiment identification utilizing various artificial intelligence techniques. Multi Class Classification with Deep Learning Algorithm shows higher precision value during the sentiment analysis.
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Cites
1 retracted
Sharma, P., & Sharma, A. K. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.09.351 · Cited by 32
RetractedSentiment analysis using product review data
Fang, X., & Zhan, J. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0015-2 · Cited by 788
ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis
Basiri, M. E., Nemati, S., Abdar, M., Cambria, E., & Acharya, U. R. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.08.005 · Cited by 745
Yang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020) · IEEE Access · doi:10.1109/access.2020.2969854 · Cited by 573
Depression detection from social network data using machine learning techniques
Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., & Ulhaq, A. (2018) · Health Information Science and Systems · doi:10.1007/s13755-018-0046-0 · Cited by 465
Detection of Depression-Related Posts in Reddit Social Media Forum
Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2019) · IEEE Access · doi:10.1109/access.2019.2909180 · Cited by 463
Jelodar, H., Wang, Y., Orji, R., & Huang, S. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3001216 · Cited by 400
Cepoiu, M., McCusker, J., Cole, M. G., Sewitch, M., Belzile, E., & Ciampi, A. (2008) · Journal of General Internal Medicine · doi:10.1007/s11606-007-0428-5 · Cited by 392
Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution
Shen, G., Jia, J., Nie, L., Feng, F., Zhang, C., Hu, T., Chua, T.-S., & Zhu, W. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/536 · Cited by 383
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Cited by
Resnik, D. B., & Hosseini, M. (2025) · AI and Ethics · doi:10.1007/s43681-024-00493-8 · Cited by 228
“• Interpreting data and images [13, 14, 21, 41].”
Arillotta, D., Floresta, G., Guirguis, A., Corkery, J. M., Catalani, V., Martinotti, G., Sensi, S. L., & Schifano, F. (2023) · Brain Sciences · doi:10.3390/brainsci13111503 · Cited by 104
Metadata
- Authors
- Babu, N. V., & Kanaga, E. G. M.
- Venue
- SN Computer Science
- Published
- 2021-11-19
- Volume
- 3
- Issue
- 1
- Pages
- 74-74
- ISSN
- 2661-8907 (ISSN-L)
- 2662-995X
- OpenAlex
- W3215748564
- Cited by
- 303
- References
- 81
- FWCI
- 15.79
- Type
- journal article
- Language
- en
- Open access
- bronze
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