کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
392595 | 664991 | 2016 | 18 صفحه PDF | دانلود رایگان |
Supervised algorithms require a set of representative labeled data for building classification models. However, labeled data are usually difficult and expensive to obtain, which motivates the interest in semi-supervised learning. This type of learning uses both labeled and unlabeled data in the training process and is particularly useful in applications such as tweet sentiment analysis, where a large amount of unlabeled data is available. Semi-supervised learning for tweet sentiment analysis, although quite appealing, is relatively new. We propose a semi-supervised learning framework that combines unsupervised information, captured from a similarity matrix constructed from unlabeled data, with a classifier. Our motivation is that such a similarity matrix is a powerful knowledge-discovery tool that can help classify unlabeled tweet sets. Our framework makes use of the well-known Self-training algorithm to induce a better tweet sentiment classifier. Experimental results in real-world datasets demonstrate that the proposed framework can improve the accuracy of tweet sentiment analysis.
Journal: Information Sciences - Volumes 355–356, 10 August 2016, Pages 348–365