A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis
Why this work is in the frame
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Bibliographic record
Abstract
The most commonly used methods in text sentiment analysis are rule-based sentiment dictionary and machine learning, with the later referring to the use of vectors to represent text followed by the use of machine learning to classify the vectors. Both methods have their limitations, including inflexibility of rules, non-prominence of sentiment words. In this paper, we design a weight distributing method combining the two methods for text sentiment analysis, by which the sentence vectors obtained can both highlight words with sentiment meanings while retaining their text information. Empirical results show that based on this new method, the accuracy rate of text sentiment analysis can reach as high as 82.1%, which means 13.9% higher than rule-based sentiment dictionary method, and 7.7% higher than TF-IDF weighting method.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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