The Multimedia Sentiment Model Based on Online Homestay Reviews
Bibliographic record
Abstract
Aiming at the fact that traditional sentiment analysis is based on text, without considering the factors such as special symbols and emoticon images, which can't fully extract the user's emotions, this paper proposes a sentiment analysis method of online homestay reviews based on image-text fusion.For text datasets, first use Word2vec to build a topic clustering model, then find the corresponding topic attribute dictionary through the topic center words, use Bayesian classifier is used for sentiment analysis, compared with SVM and decision tree methods, to evaluate the effect; For the picture dataset, Convolutional Neural Network (CNN) model is initialized by parameter migration, and image sentiment classification model is obtained by fine-tuning training of CNN model after parameter migration; Finally, the fusion method is designed to calculate the emotional probability of image-text, then judge the emotional polarity and compare it with the user's score, The accuracy rate is 88.6%, which is higher than that of text sentiment analysis model or image sentiment analysis model.The experimental results show that the sentiment analysis of image-text fusion has better classification effect on image-text reviews and more effectively avoid the problem of inconsistency between user ratings and the emotion expressed in comments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".