Application of Sentiment Analysis to Explore the Connection Between Public Sentiment and Stock Price Movement by Python
Bibliographic record
Abstract
This study aims to research the relationship between sentiments of public and the trend of the stock. Specially, it focuses on analyzing how positive or negative sentiment correlated to the trend of stocks. In methodology, this research investigates the predictive power of public sentiment on stock market trend by analyzing user’s comments on Reddit by utilizing Python Reddit API Wrapper (PRAW) to systematically web-scrape relevant financial discussion from a subreddit called ‘stocks’. After the data collecting, preprocessing step is conducted to removing noise and standardize text. Eventually, sentiment analysis is applied to analyze the sentiments of comments by using Natural Language Processing (NLP) techniques. This approach allows for an in-depth examination of the correlation between the sentiments expressed in Reddit comments and subsequent stock price fluctuations by comparing the outcome and the actual price movement. Eventually, through the comparison between the sentiment analysis result and actual stocks movement, the sentiment of public comments shares the same trend with the market movement.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".