Sentiment Analysis of StockTwits Using Transformer Models
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
Forecasting stock price movements is an important task for investors and traders, though still very difficult due to the unstable nature and complex behavior of the stock market. Accordingly, each piece of information related to stock prices can be deemed useful. In this regard, social media platforms provide a vast amount of information that can be used to predict stock movements. In this study, we compare the performances of various traditional, deep learning, and state-of-art pre-trained transformer models for text classification of tweets related to the stock market, which are obtained through a financial microblog, StockTwits. For this purpose, we collected 100,000 labeled messages of five stocks, namely, Apple Inc. (AAPL), Amazon (AMZN), Boeing Co, (BA), Walt Disney Co. (DIS), and the SPDR S&P 500 ETF Trust (SPY) for a period between December 2019 and June 2020. We used logistic regression and random forest as traditional classifiers and Long Short Term Memory and Gated Recurrent Unit as the deep learning algorithms, and BERT, DistillBERT, RoBERTa, and XLNet as the state-of-art transformer models to classify tweets as either “bearish” or “bullish”. Our numerical study showed that RoBERTa outperformed traditional classifiers and deep learning algorithms in terms of average F1-scores.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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