Using Machine Learning to Analyse User Psychology in Social Media
Bibliographic record
Abstract
In this study, we attempt to identify the emotion levels, such as positive, negative, & neutral feelings, from postings and comments on social networking sites on depression.Social media sites like Facebook and Twitter are becoming effective for helping those in need who require extra care or attention in terms of mental support.They are also utilized for communication and network development among relationships.There are several depressive support groups on Facebook, and they are quite helpful in giving the sufferers mental assistance.In this study, we attempt to formalize the posts and comments on depression into a succinct lexical database and identify the emotion levels from each occurrence.The complete amount of work has been divided into two sections: sentiment analysis and the use of machine learning techniques to examine the capability of extracting sentiment from such a unique category of texts.To determine the sentiment levels, we used the Python textblob module and typical machine learning techniques on the linguistic characteristics.For each of the classifiers, we have calculated the precision, recall, F-measure, accuracy, and ROC values.Random Forest outperformed the other classifiers, successfully classifying 60.54% of the instances.We think that conducting sentiment analysis on a particular class of texts may inspire additional research into how natural language is understood.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".