TED Talks Comments Sentiment Classification Using Machine Learning Algorithms
Bibliographic record
Abstract
This research presents a comparative analysis of sentiment analysis techniques applied to user comments on YouTube, with a specific focus on TED talks.The proliferation of social media platforms has provided individuals with unprecedented opportunities to express their opinions and emotions.YouTube, as a leading video-sharing platform, has become a significant hub for user-generated content and discussions on a wide range of topics.In light of the exponential growth of unstructured and semi-structured data, sentiment analysis plays a critical role in extracting valuable emotional insights from online interactions.To evaluate sentiments expressed in YouTube comments, a self-created and meticulously labeled dataset comprising user comments was employed.The study compared the performance of five ML techniques: NB, SVM, RF, KNN, and DT.The performance of the classifiers was evaluated using key evaluation metrics such as Precision, Recall, and F1score.The findings of this research offer valuable insights into the efficacy of various machine learning techniques for sentiment analysis in the context of YouTube comments on TED talks.Among the classifiers, SVM demonstrated the highest Precision, Recall, and F1-score, indicating its effectiveness in accurately identifying sentiment in YouTube comments.Random Forest and Decision tree also displayed competitive performance, while KNN and Naï ve Bayes exhibited slightly lower accuracy.These results provide researchers and practitioners with valuable information to make informed decisions regarding the selection of appropriate ML techniques for SA tasks on social media platforms.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".