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arXiv · 2008.11573

Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance

Selim F. Yilmaz·E. Batuhan Kaynak·Aykut Koç·Hamdi Dibeklioğlu·Suleyman S. Kozat
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Abstract

We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics and social sciences. In particular, we introduce a sentiment analysis framework in multi-label setting as it obeys Plutchik wheel of emotions. We introduce a novel dynamic weighting method that balances the contribution from each class during training, unlike previous static weighting methods that assign non-changing weights based on their class frequency. Moreover, we adapt the focal loss that favors harder instances from single-label object recognition literature to our multi-label setting. Furthermore, we derive a method to choose optimal class-specific thresholds that maximize the macro-f1 score in linear time complexity. Through an extensive set of experiments, we show that our method obtains the state-of-the-art performance in 7 of 9 metrics in 3 different languages using a single model compared to the common baselines and the best-performing methods in the SemEval competition. We publicly share our code for our model, which can perform sentiment analysis in 100 languages, to facilitate further research.

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Selim F. Yilmaz, E. Batuhan Kaynak, Aykut Koç, Hamdi Dibeklioğlu, Suleyman S. Kozat. 2020-08-26. Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance. https://doi.org/10.1109/tnnls.2021.3094304

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