PREDICTING LISTENER’S MOOD BASED ON MUSIC GENRE: AN ADAPTED REPRODUCED MODEL OF RUSSELL AND THAYER
Bibliographic record
Abstract
Mood has presently received growing consideration as an interesting technique for organizing and accessing music. Stress which changes individual mood is a major physical and psychological problem of individuals today. Many researches have been conducted based on this study of mood, particularly in the U.S.A, Canada, Europe, and some part in Asia. However, while these studies are important, and help to solve the problem of mood change, still, researchers were unable to look into this important aspect in one of the 25 rapid growth markets in the world-Malaysia. In solving this problem, this study suggested using music genre as an influence mechanism to predict mood and again identify what kind of classified musical genre that can be used to predict certain mood. This study adapts and reproduces a model of Russell and Thayer to categorize moods. A total population of 245 university students of both sexes, aged from 18-56 and above, married and single, different educational level, race, and religions were used to achieved the objective of this study. The data was analyzed using SPSS version 20. The analysis results were presented based on majority and popularity of respondents. The findings indicate that the result of this study is 60%-80% percent positive on both part A and Part B due to the higher population respondents of the investigation. Hence, based on the findings, the study clearly interprets and presents an encouraging methodology that predicts the mood of the listener's with a positive outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".