COAL: convolutional online adaptation learning for opinion mining

Conference Publication ResearchOnline@JCU

Chaturvedi, Iti;Ragusa, Edoardo;Gastaldo, Paolo;Cambria, Erik

Abstract

Thanks to recent advances in machine learning, AI is the new engine and data is the new coal. Mining this 'coal' from the ever-growing Social Web, however, can be a formidable task. In this work, we address this problem in the context of sentiment analysis using convolutional online adaptation learning (COAL). In particular, we consider semi-supervised learning of convolutional features, which we use to train an online model. Such a model, which can be trained in one domain but also used to predict sentiment in other domains, outperforms the baseline in the range of 5-20%.

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ICDMW 2020: International Conference on Data Mining

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ISBN/ISSN

978-1-7281-9012-9

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Sorrento, Italy

Publisher

Institute of Electrical and Electronics Engineers

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Piscataway, NJ, USA

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DOI

10.1109/ICDMW51313.2020.00012