Sentiment Analysis of the Documentary Film Ice Cold on Twitter: A Comparative Study of Machine Learning and Rule-Based Methods
Bibliographic record
Abstract
Sentiment analysis in the film industry represents a significant research area, particularly when applied to specific films or genres.The documentary film Ice Cold: Murder, Coffee, and Jessica Wongso provides a pertinent case study for such analysis.The Support Vector Machine (SVM) is employed to classify the sentiments expressed in reviews, social media posts, and other text data related to the film for sentiment analysis.This study analyzes Twitter sentiment data about the documentary film Ice Cold: Murder, Coffee, and Jessica Wongso by using three classification methods: SVM, Multinomial Naive Bayes (MultinomialNB), and Random Forest.The results indicate that SVM achieved the highest accuracy of 74.62%, followed by Random Forest at 74.43%.The MultinomialNB method yielded a lower accuracy of 62.40%.Additionally, two rule-based methods, Vader and Text Blob, were evaluated.Vader achieved an accuracy of 99.17%, while TextBlob reached 58.16%.These results highlight the performance differences between classification methods and rule-based methods, emphasizing the importance of choosing the appropriate method based on the data characteristics.Overall, SVM demonstrated the most effective method among the machine learning methods evaluated in this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".