COAL: convolutional online adaptation learning for opinion mining
Chaturvedi, Iti, Ragusa, Edoardo, Gastaldo, Paolo, and Cambria, Erik (2020) COAL: convolutional online adaptation learning for opinion mining. In: International Conference on Data Mining. pp. 15-22. From: ICDMW 2020: International Conference on Data Mining, 17-20 November 2020, Sorrento, Italy.
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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%.
| Item ID: | 66232 |
|---|---|
| Item Type: | Conference Item (Research - E1) |
| ISBN: | 978-1-7281-9012-9 |
| Keywords: | Sentiment Prediction, Domain Adaptation, Online SVM |
| Copyright Information: | (C) IEEE |
| Additional Information: | Virtual Event. |
| Date Deposited: | 17 Mar 2021 04:03 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461106 Semi- and unsupervised learning @ 70% 47 LANGUAGE, COMMUNICATION AND CULTURE > 4704 Linguistics > 470403 Computational linguistics @ 30% |
| SEO Codes: | 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 50% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50% |
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