A Survey of Using Weakly Supervised and Semi-Supervised for Cross-Domain Sentiment Classification

Supervised machine learning techniques can analyze sentiment very effectively. However, in many languages, there are few appropriate data for training sentiment classifiers. Thus, they need a large corpus of training data. In this paper, weakly-supervised techniques using a large collection of unlabeled text to determine sentiment is presented. The performance of this method maybe less depends on the domain, topic and time period represented by the testing data. In addition, semi-supervised classification using a sentiment-sensitive thesaurus is mentioned. It can be applicable when it does not have any labeled data for a target domain but have some labeled data for other multiple domains designated as the source domains. This method can learn efficiently from multiple source domains. The results show that the weakly-supervised techniques are suitable for applications requiring sentiment classification across some domains and semi-supervised techniques can learn efficiently from multiple source domains.