Tackling the Problem of Class Imbalance in Multi-class Sentiment Classification: An Experimental Study
[ 1 ] Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | [ P ] pracownik
2019
artykuł naukowy
angielski
- sentiment analysis
- imbalanced data
- multi-class learning
- data difficulty factors
- text classification
EN 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.
06.06.2019
151 - 178
CC BY-NC-ND (uznanie autorstwa - użycie niekomercyjne - bez utworów zależnych)
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