From Transformers to Voting Ensembles for Interpretable Sentiment Classification: A Comprehensive Comparison
This study conducts an in-depth investigation of the performance of six transformer models using 12 different datasets—10 with three classes and two with two classes—on sentiment classification. We use these six models and generate all combinations of triple schema ensembles, Majority and Soft vote....
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/5/167 |
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| author | Konstantinos Kyritsis Charalampos M. Liapis Isidoros Perikos Michael Paraskevas Vaggelis Kapoulas |
| author_facet | Konstantinos Kyritsis Charalampos M. Liapis Isidoros Perikos Michael Paraskevas Vaggelis Kapoulas |
| author_sort | Konstantinos Kyritsis |
| collection | DOAJ |
| description | This study conducts an in-depth investigation of the performance of six transformer models using 12 different datasets—10 with three classes and two with two classes—on sentiment classification. We use these six models and generate all combinations of triple schema ensembles, Majority and Soft vote. In total, we compare 46 classifiers on each dataset and see in one case up to a 7.6% increase in accuracy on a dataset with three classes from an ensemble scheme and, in a second case, up to 8.5% increase in accuracy on a dataset with two classes. Our study contributes to the field of natural language processing by exploring the reasons for the predominance, in this particular task, of Majority vote over Soft vote. The conclusions are drawn after a thorough investigation of the classifiers that are co-compared with each other through reliability charts, analyses of the confidence the models have in their predictions and their metrics, concluding with statistical analyses using the Friedman test and the Nemenyi post-hoc test with useful conclusions. |
| format | Article |
| id | doaj-art-fd1dbb402fc24d56ab0aa38c36e224e3 |
| institution | DOAJ |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-fd1dbb402fc24d56ab0aa38c36e224e32025-08-20T03:14:41ZengMDPI AGComputers2073-431X2025-04-0114516710.3390/computers14050167From Transformers to Voting Ensembles for Interpretable Sentiment Classification: A Comprehensive ComparisonKonstantinos Kyritsis0Charalampos M. Liapis1Isidoros Perikos2Michael Paraskevas3Vaggelis Kapoulas4Department of Electrical & Computer Engineering, University of Peloponnese, 26334 Patras, GreeceDepartment of Mathematics, University of Patras, 26504 Patras, GreeceDepartment of Electrical & Computer Engineering, University of Peloponnese, 26334 Patras, GreeceDepartment of Electrical & Computer Engineering, University of Peloponnese, 26334 Patras, GreeceComputer Technology Institute & Press “Diophantus”, 26504 Patras, GreeceThis study conducts an in-depth investigation of the performance of six transformer models using 12 different datasets—10 with three classes and two with two classes—on sentiment classification. We use these six models and generate all combinations of triple schema ensembles, Majority and Soft vote. In total, we compare 46 classifiers on each dataset and see in one case up to a 7.6% increase in accuracy on a dataset with three classes from an ensemble scheme and, in a second case, up to 8.5% increase in accuracy on a dataset with two classes. Our study contributes to the field of natural language processing by exploring the reasons for the predominance, in this particular task, of Majority vote over Soft vote. The conclusions are drawn after a thorough investigation of the classifiers that are co-compared with each other through reliability charts, analyses of the confidence the models have in their predictions and their metrics, concluding with statistical analyses using the Friedman test and the Nemenyi post-hoc test with useful conclusions.https://www.mdpi.com/2073-431X/14/5/167transformer modelsensemblesclassifierssentiment classificationmachine learning |
| spellingShingle | Konstantinos Kyritsis Charalampos M. Liapis Isidoros Perikos Michael Paraskevas Vaggelis Kapoulas From Transformers to Voting Ensembles for Interpretable Sentiment Classification: A Comprehensive Comparison Computers transformer models ensembles classifiers sentiment classification machine learning |
| title | From Transformers to Voting Ensembles for Interpretable Sentiment Classification: A Comprehensive Comparison |
| title_full | From Transformers to Voting Ensembles for Interpretable Sentiment Classification: A Comprehensive Comparison |
| title_fullStr | From Transformers to Voting Ensembles for Interpretable Sentiment Classification: A Comprehensive Comparison |
| title_full_unstemmed | From Transformers to Voting Ensembles for Interpretable Sentiment Classification: A Comprehensive Comparison |
| title_short | From Transformers to Voting Ensembles for Interpretable Sentiment Classification: A Comprehensive Comparison |
| title_sort | from transformers to voting ensembles for interpretable sentiment classification a comprehensive comparison |
| topic | transformer models ensembles classifiers sentiment classification machine learning |
| url | https://www.mdpi.com/2073-431X/14/5/167 |
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