Evaluating Sentiment C1assifiers for Bitcoin Tweets in Price Prediction Task
Bibliographic record
Abstract
Bitcoin alongside other cryptocurrencies became one of the largest trends recently, due to its redefinition of the concept of money, and its price fluctuation. Especially on the social media, people keep discussing Bitcoin topics, consulting, and advising about cryptocurrency trading. This paper explores the relationship between Twitter feed on Bitcoin and sentiment analysis of it, comparing and evaluating different data mining classifiers and deep learning methods that might help in better sentiment classification of Bitcoin tweets, the study uses different language modeling approaches, such as tweet embedding and N-Gram modeling. We also evaluate the quality of automated sentiment classification in comparison to manually assigned sentiment labeling. The results show that the manual approach gives significantly better results in some datasets, and superior performance of MLP, WiSARD and decision tree methods. On the other hand, R-Auto Tweets Sentiment (RATS) gives more stable performance overall datasets. using time-series, we found partial correlation between Bitcoin price fluctuation and sentiment class accuracy fluctuations using different machine learning algorithms.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".