Bibliographic record
Abstract
Although successfully employed to reduce error rates of difficult pattern recognition problems, multi-classifier systems (MCS) are not in widespread use in the field of Sentiment Analysis and Opinion Mining. The motivation of using a MCS stems from the fact that different classifiers usually make different errors on different samples. By using just the best classifier, it is possible to loose valuable information contained in the other sub optimal classifiers. In this work, we take advantage of unigrams, big rams and trig rams to design a multi-classifier system for Sentiment Analysis and Opinion Mining. Three different Naive Bayes classifiers are trained--each one with a specific set of features-- , and then combined in the ROC space by using the Iterative Boolean Combination (IBC) technique. IBC iteratively combines the ROC curves produced by different classifiers using all Boolean functions, and does not require prior assumption that the classifiers are statistically independent. An experimental study investigates the advantage of using the proposed MCS, over each individual classifier, in classifying Twitter messages as positive or negative. The Stanford University's Twitter database is employed for this task. As real-world application, the proposed MCS is used to identify the sentiment of electors regarding the main candidates for the 2012 United States Presidential Elections. Results indicate that the proposed MCS can provide useful information about people's opinions that are comparable to conventional opinion polls.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.092 | 0.071 |
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 source (direct Gemma or distilled Codex), 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".