Application of Deep Learning in Cross-Lingual Sentiment Analysis for Natural Language Processing
Bibliographic record
Abstract
Natural language processing and sentiment analysis are important research areas in the field of artificial intelligence. With the development of globalization, cross-lingual sentiment analysis has become a challenging task. This paper focuses on the application of deep learning in natural language processing for cross-lingual sentiment analysis. Firstly, an overview of natural language processing and sentiment analysis is provided, including their definitions and development history. Then, the challenges in cross-lingual sentiment analysis are discussed, including the influence of language and cultural differences on sentiment identification, as well as the issues in data annotation and cross-lingual data. Next, the application of deep learning in natural language processing and sentiment analysis is highlighted, covering the principles of deep learning algorithms, text representation and feature extraction methods, and application cases in sentiment analysis. Furthermore, a deep learning approach for cross-lingual sentiment analysis is proposed, presenting the task definition, datasets, models, and evaluation metrics in detail. Finally, through experimental results and analysis, the performance of the cross-lingual sentiment analysis models is evaluated, and the advantages, limitations, and future directions of deep learning methods in this field are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".