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.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: https://ieeexplore.ieee.org/document/934...
 
5


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%
Downloads: Total: 5
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page