An Ensemble Sentiment Classification System of Twitter Data for Airline Services Analysis
Why this work is in the frame
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Bibliographic record
Abstract
In airline service industry, it is difficult to collect data about customers' feedback by questionnaires, but Twitter provides a sound data source for them to do customer sentiment analysis. However, little research has been done in the domain of Twitter sentiment classification about airline services. In this paper, an ensemble sentiment classification strategy was applied based on Majority Vote principle of multiple classification methods, including Naive Bayes, SVM, Bayesian Network, C4.5 Decision Tree and Random Forest algorithms. In our experiments, six individual classification approaches, and the proposed ensemble approach were all trained and tested using the same dataset of 12864 tweets, in which 10 fold evaluation is used to validate the classifiers. The results show that the proposed ensemble approach outperforms these individual classifiers in this airline service Twitter dataset. Based on our observations, the ensemble approach could improve the overall accuracy in twitter sentiment classification for other services as well.
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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 | 0.000 |
| 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 it