Improving Performance Sentiment Movie Review Classification Using Hybrid Feature TFIDF, N-Gram, Information Gain and Support Vector Machine
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
The use of online movie streaming media has increased significantly, particularly among movie enthusiasts.However, fan comments are frequently informal and comprise informal language, subjectivity, and contexts that reflect their preferences.A significant challenge in sentiment analysis of movie reviews is how to classify sentiments in reviews that are often unstructured and subjective.This study aims to improve the accuracy of sentiment classification in movie reviews by proposing several methods, including a hybrid TF-IDF+N-Gram model that can extract pertinent information from word and phrase sequences in reviews.Then, feature selection with Information Gain (IG) is performed to identify the most informative sentiment classification features.This strategy seeks to overcome informal language and noise to improve review context comprehension.The results demonstrated a significant gain in the accuracy of sentiment classification.TFIDF+Bigram+IG achieved 78% accuracy (up 8% from 70% previously), and TFIDF+Trigram+IG achieved 66% accuracy (up 22% from 44% previously).Using this hybrid model, the study significantly enhanced the accuracy of sentiment classification, thereby enhancing the performance of SVM in the face of complex movie evaluations.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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 it