<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Sentiment classification is an important task which gained extensive attention both in academia and in industry. Many issues related to this task such as handling of negation or of sarcastic utterances were analyzed and accordingly addressed in previous works. However, the issue of class imbalance which often compromises the prediction capabilities of learning algorithms was scarcely studied. In this work, we aim to bridge the gap between imbalanced learning and sentiment analysis. An experimental study including twelve imbalanced learning preprocessing methods, four feature representations, and a dozen of datasets, is carried out in order to analyze the usefulness of imbalanced learning methods for sentiment classification. Moreover, the data difficulty factors — commonly studied in imbalanced learning — are investigated on sentiment corpora to evaluate the impact of class imbalance.</p></abstract>

Sentiment classification is an important task which gained extensive attention both in academia and in industry. Many issues related to this task such as handling of negation or of sarcastic utterances were analyzed and accordingly addressed in previous works. However, the issue of class imbalance which often compromises the prediction capabilities of learning algorithms was scarcely studied. In this work, we aim to bridge the gap between imbalanced learning and sentiment analysis. An experimental study including twelve imbalanced learning preprocessing methods, four feature representations, and a dozen of datasets, is carried out in order to analyze the usefulness of imbalanced learning methods for sentiment classification. Moreover, the data difficulty factors — commonly studied in imbalanced learning — are investigated on sentiment corpora to evaluate the impact of class imbalance.

Tackling the Problem of Class Imbalance in Multi-class Sentiment Classification: An Experimental Study

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Sentiment classification is an important task which gained extensive attention both in academia and in industry. Many issues related to this task such as...

Tackling the Problem of Class Imbalance in Multi-class Sentiment Classification: An Experimental Study

Online veröffentlicht: 06. Juni 2019

Seitenbereich: 151 - 178

Eingereicht: 18. Jan. 2019

Akzeptiert: 24. Feb. 2019

DOI: https://doi.org/10.2478/fcds-2019-0009

Schlüsselwörter
sentiment analysis, imbalanced data, multi-class learning, data difficulty factors, text classification

© 2019 Mateusz Lango, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Tackling the Problem of Class Imbalance in Multi-class Sentiment Classification: An Experimental Study

Online veröffentlicht: 06. Juni 2019

Seitenbereich: 151 - 178

Eingereicht: 18. Jan. 2019

Akzeptiert: 24. Feb. 2019

DOI: https://doi.org/10.2478/fcds-2019-0009

Schlüsselwörtersentiment analysis, imbalanced data, multi-class learning, data difficulty factors, text classification

© 2019 Mateusz Lango, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
sentiment analysis, imbalanced data, multi-class learning, data difficulty factors, text classification