A Comparative Study on Machine Learning Approaches for Sentiment Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sentiment analysis plays a pivotal role in the operations of online product companies. User reviews are taken into account by others when they search for products, forming the cornerstone for delivering the right product based on user sentiments through sentiment analysis. Sentiment analysis involves the process of collecting, analyzing, and recommending reviews, which are often extensive and contain multiple paragraphs of content. This paper presents a comparative analysis of various machine learning models used to conduct sentiment analysis on customer reviews of Amazon products within the Electronics category. The initial models under scrutiny for our analysis include Logistic Regression, Decision Tree, Naive Bayes Classifier, Random Forest, Support Vector Machines, and BERT Model. The experimental result show that BERT classifier achieves higher accuracy when compare with other machine learning models.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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 it