Published 2020
| Version v1
Publication
COAL: Convolutional Online Adaptation Learning for Opinion Mining
- Creators
- Chaturvedi I.
- Ragusa E.
- Gastaldo P.
- Cambria E.
Description
Thanks to recent advances in machine learning, some say 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%.
Additional details
- URL
- https://hdl.handle.net/11567/1061994
- URN
- urn:oai:iris.unige.it:11567/1061994
- Origin repository
- UNIGE