Lightweight Scheme to Capture Stock Market Sentiment on Social Media Using Sparse Attention Mechanism: A Case Study on Twitter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over through the years, people have invested in stock markets in order to maximize their profit from the money they possess. Financial sentiment analysis is an important topic in stock market businesses since it helps investors to understand the overall sentiment towards a company and the stock market, which helps them make better investment decisions. Recent studies show that stock sentiment has strong correlations with the stock market, and we can effectively monitor public sentiment towards the stock market by leveraging social media data. Consequently, it is crucial to develop a model capable of reliably and quickly capturing the sentiment of the stock market. In this paper, we propose a novel and effective sequence-to-sequence transformer model, optimized using a sparse attention mechanism, for financial sentiment analysis. This approach enables investors to understand the overall sentiment towards a company and the stock market, thereby aiding in better investment decisions. Our model is trained on a corpus of financial news items to predict sentiment scores for financial companies. When benchmarked against other models like CNN, LSTM, and BERT, our model is “lightweight” and achieves a competitive latency of 10.3 ms and a reduced computational complexity of 3.2 GFLOPS—which is faster than BERT’s 12.5 ms while maintaining higher computational complexity. This research has the potential to significantly inform decision making in the financial sector.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it