Review of machine learning with sentimental analysis method for cross-model stock price prediction
Bibliographic record
Abstract
Stock market prediction has been a popular area of research for years. Machine learning, as a fast-developing popular algorithm, is applied to stock market prediction by many previous researchers to better solve the time series involving problems over the changing prices. Different from traditional machine learning algorithms that focus on stock prices only, this paper gives a brief description and review of stock price predictions models that contain sentimental analysis over the recent paper works. Different from the prices in number format, sentimental analysis is more based on textual information extraction and mining, converting them into usable input to pass to a prediction model. This extends the prediction model's takeable input domain and strengthens the accuracy. To better classify the differences between the models, discussion and introduction are given based on different model types about whether they are traditional type or deep neural network embedded. Even though traditional types of models are more popular for sentimental analysis and neural networks perform better in prediction tasks, traditional methods are relatively easy to build or train with more explainability, compared to deep learning models suitable for larger data sets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".