Leveraging Big Data Analytics and Machine Learning Techniques for Sentiment Analysis of Amazon Product Reviews in Business Insights
Bibliographic record
Abstract
Purpose: Satisfactory consumer feedback results from sentiment research which enables product quality enhancement. The research examines Amazon product review data through machine learning methods for sentiment analysis to extract important insights that improve customer experience. Materials and Methods: A Gradient Boost Classifier stands at the core of the proposed method which conducts sentiment analysis operations. The preliminary data treatment includes punctuation removal and stop word filtering followed by text tokenization. Feature extraction is performed using the Bag of Words (BoW) technique. The data is split into training and testing sets, and the models are evaluated using F1-score, recall, accuracy, and precision. Comparative analysis is conducted with Logistic Regression (LR), Naïve Bayes (NB), and Recursive Neural Network for Multiple Sentences (RNNMS). Findings: Among the tested models, the Gradient Boost Classifier consistently outperforms others, achieving a robust performance of 82% across all evaluation metrics. This highlights its superior classification capability in sentiment analysis tasks. Unique Contributions to Theory, Practice and Policy: While Gradient Boosting demonstrates high accuracy, future research could explore more advanced models and techniques, such as transformer-based architectures, to enhance sentiment classification across diverse product categories and address more nuanced sentiment patterns
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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".