Emoji-Integrated Polyseme Probabilistic Analysis Model: Sentiment Analysis of Short Review Texts on Library Service Quality
Bibliographic record
Abstract
It is a great challenge to understand user evaluation of library service quality based on short review texts. This is because short texts are limited in length and lack context support. What is worse, the polysemes and emojis in short texts make the literal emotions of these texts rather ambiguous and variable. The variability is often overlooked in previous research on service quality evaluation, which reduces the accuracy of automatic analysis methods. Considering the effects of polysemes and emojis in short texts, this paper introduces probabilistic linguistic term sets (PLTS) and support vector machine (SVM) to establish a framework for emotional classification of library service quality (FECLSQ). Every word and emoji were converted into the corresponding PLTS to depict the probability of the word/emoji belonging to each sentiment polarity, making short text sentiment analysis more accurate. Through supervised learning of corpuses, the authors established the PLTSs of polysemes, and context sentiment weight dictionary (CSWD), and coupled them with the FECLSQ for sentiment analysis and application of text sets with various themes. The proposed approach was utilized to correctly evaluate library service quality.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.012 |
| 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) | 0.004 | 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".