Live Sentiment Analysis Using Multiple Machine Learning and Text Processing Algorithms
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
Due to the massive amount of data being generated on the platform, Twitter has been the subject of numerous sentiment analysis studies. Such social network services generate massive unstructured data streams which make working with them very challenging. The aim of this study is to reliably analyze the sentiment of trending tweets in the Twitter API data stream using a combination of different algorithms to achieve a consensus. The methods we implemented include Support-Vector Machine, Naive Bayes, Textblob, and Lexicon Approach. The hypothesis is that using these methods together would enable us to get more accurate results. Using a labeled dataset to test our model, the results show that the combination of these four algorithms all together performed best with an overall accuracy of 68.29%. We conclude that our combination method of analysis is suitable and fast enough for our data stream and also accurate for analyzing sentiment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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