Analysis of Consumer Sentiments towards Online Shopping Using Context-Free Grammar and Deep Learning
Bibliographic record
Abstract
Consumer reviews represent customer sentiment.They provide valuable insights that businesses can use to improve product or service quality and meet customer needs.Therefore, this research presents an analysis of consumer sentiments towards online shopping using Context-Free Grammar and Deep Learning.It tested the effectiveness of classifying consumer sentiments from 3,600 product or service reviews.The dataset included three types of sentiment categories: Positive, Negative, and Neutral.Context-Free Grammar (CFG) was used to assign term functions as sentiment indicators, and Term Frequency-Inverse Document Frequency (TF-IDF) was used for feature selection.A threshold value was then assigned to each term that represented the sentiment categories.The dataset was divided into 15-fold cross-validation to test the effectiveness of the model before Deep Learning algorithm was used to classify the sentiments.Deep Learning algorithms have the capability to learn complex relationships between terms, allowing for precise sentiment classification.The evaluation showed that using CFG and TF-IDF for term weighting improved the selection of keyword features, leading to significantly more precise sentiment classification.The average Precision, Recall, and F-measure were higher than exclusively using TF-IDF.Moreover, the determination of an appropriate threshold value reduced data complexity without affecting the accuracy of sentiment classification.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".