YouTube Comment Sentiment Classification System
Bibliographic record
Abstract
With more than 2 billion viewers per month, YouTube is the most widely used video-sharing website worldwide. On this website, users can watch, upload, and share videos covering a wide range of subjects. YouTube comments include facts, opinions, and responses to videos in addition to starting discussions. The number of YouTube comments makes it difficult to manually analyze them all. The study of reading, understanding, and creating text in human languages encompasses a broad range of methods and techniques under the umbrella of natural language processing or NLP. The primary goal of the research is to find and analyze YouTube comments, which, when used with natural language processing algorithms, might be beneficial for the channels' continued development. One of the NLP methods used for this research was tokenization, which is used to break down text into individual words or tokens. Stemming and lemmatization are used to reduce the root words and normalize the variation. Categorization is performed by identifying the named entities, such as people, organizations, and locations. The machine translation was used to convert the comments from one language to another.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".