Deep Learning-Based Analysis of Emotional Posture in Chinese Literary Works
Bibliographic record
Abstract
In order to help readers analyze the emotions covered in Chinese literature and assist them in reading classic Chinese literature.In this paper, we propose a cross-granularity text sentiment analysis model based on interactive attention.Firstly, we carry out the preparation work for text processing of Chinese literary works, and for the shortcomings of the original word vector representation model that lacks the ability to distinguish text weights, we improve the TF-IDF algorithm in terms of weight imbalance of similar corpus, and combine the TF-IDF algorithm with the Word2vec word vector transformation model to enhance the weight of keyword vectors.A cross-granularity text sentiment analysis model based on Interactive Attention Network is proposed, in which the Interactive Attention Network is responsible for receiving the feature vectors encoded by RoBERTa and Word2vec respectively, capturing the bidirectional semantic information of long and difficult sentences.The sentiment analysis model in this paper has different degrees of accuracy and MF1 values on three different datasets.The method is able to provide a better sentiment visualization of some of the story lines of the classic Chinese literary work "Home", and it can accurately capture the emotional relationships in Chinese literature, which helps to assist readers in reading Chinese literary classics.
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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 | 0.001 |
| 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".