ReviewSN computer science2022
Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review.
Review in SN computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methodologies for Monitoring Mental Health on Twitter: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Scrolling for Surgery: Artificial Intelligence-Powered Evaluation of Labiaplasty Discourse on TikTok.Aesthetic plastic surgery · 2026Observational
- A psychological education model integrating artificial intelligence-based BERT in physical education.Scientific reports · 2026Article
- Reducing social biases in text-based emotion prediction using semantic blinding and semantic propagation graph neural networks.Scientific reports · 2026Article
- Comparing Images of Depression in Mass Media and AI-Generated Pictures: Mixed Methods Study.JMIR human factors · 2026Article
- Emotion meets coordination: Designing multi-agent LLMs for fine-grained user sentiment detection on social media.PloS one · 2026Article
- Domain-informed density extraction for robust mental health classification of long social media posts.Frontiers in artificial intelligence · 2026Article
- Understanding Cancer Survivorship Care Needs Using Amazon Reviews: Content Analysis, Algorithm Development, and Validation Study.JMIR cancer · 2025Article
- Anxiety and Depression are Associated with More Distorted Thinking on Social Media: A Longitudinal Multi-Method Study.Cognitive therapy and research · 2025Article
- The data visualization and intelligent text analysis for effective evaluation of English language teaching.Scientific reports · 2025Article
- BERT and BERTopic for screening clinical depression on open-ended text messages collected through a mobile application from older adults.BMC public health · 2025Article
- The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool.AI and ethics · 2025Article
- Article
- Analyzing student mental health with RoBERTa-Large: a sentiment analysis and data analytics approach.Frontiers in big data · 2025Article
- This and that in depression: Cross-linguistic semantic effects.PLOS mental health · 2025Article
- Emotion-RGC net: A novel approach for emotion recognition in social media using RoBERTa and Graph Neural Networks.PloS one · 2025Article
- What Is the Attitude of Romanian Smallholders Towards a Ground Mole Infestation? A Study Using Topic Modelling and Sentiment Analysis on Social Media and Blog Discussions.Animals : an open access journal from MDPI · 2024Article
- Screening for Depression Using Natural Language Processing: Literature Review.Interactive journal of medical research · 2024Review
- Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study.Journal of medical Internet research · 2024Article
- Early Detection of Mental Health Crises through Artifical-Intelligence-Powered Social Media Analysis: A Prospective Observational Study.Journal of personalized medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.