Simulating real-time tweet sentiment analysis by different machine learning methods based on spark
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
Sentiment analysis is essential since it benefits many fields, such as politics and economics. Because much data is generated every moment, a real-time processing system can efficiently analyze sentiment. This paper uses Spark to simulate real-time tweet sentiment analysis, and compares the performances of three machine learning methods, Logistic Regression, Naive Bayes, and Decision Tree. The idea of the real-time tweet sentiment analysis system is using Spark Streaming to send a batch of tweets every fixed period to a machine learning pipeline to predict the emotions of tweets. In the pipeline, tweets will be tokenized first, then the stop words in tweets will be removed. After that, the author uses TF-IDF to extract features, transferring data from unstructured to structured. The last stage is using the machine learning method to predict the sentiments of tweets. By comparing, Logistic Regression has the best performance, and the second one is Naive Bayes, Decision Tree performs not as well as the other two methods.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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