SSentiA: A Self-supervised Sentiment Analyzer for classification from unlabeled data
In recent years, supervised machine learning (ML) methods have realized remarkable performance gains for sentiment classification utilizing labeled data. However, labeled data are usually expensive to obtain, thus, not always achievable. When annotated data are unavailable, the unsupervised tools ar...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-06-01
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| Series: | Machine Learning with Applications |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827021000074 |