Open Domain Targeted Sentiment Classification Using Semi-Supervised Dynamic Generation of Feature Attributes

Shadi Abudalfa (King Fahd University of Petroleum and Minerals, Saudi Arabia)

Moataz Ahmed (King Fahd University of Petroleum and Minerals, Saudi Arabia)

Abstract: Microblogging services have been significantly increased nowadays and enabled people to share conveniently their sentiments (opinions) with regard to matters of concerns. Such sentiments have shown an impact on many fields such as economics and politics. Different sentiment analysis approaches have been proposed in the literature to predict automatically sentiments shared in micro-blogs (e.g., tweets). A class of such approaches predicts opinion towards specific target (entity); this class is referred to as target-dependent sentiment classification. Another class, called open domain targeted sentiment classification, extracts targets from the micro-blog and predicts sentiment towards them. In this research work, we propose a new semi-supervised learning technique for developing open domain targeted sentiment classification by using fewer amounts of labelled data. To the best of our knowledge, our model represents the first semi-supervised technique that is proposed for open domain targeted sentiment classification. Additionally, we propose a new supervised learning model for improving accuracy of open domain targeted sentiment classification. Moreover, we show for the first time that SVM HMM is able to improve accuracy of open domain targeted sentiment classification. Experimental results show that our proposed technique outperforms other prominent techniques available in the literature.

Keywords: open domain, polarity classification, semi-supervised learning, social opinions, targeted sentiment analysis, text mining

Categories: H.3.3, I.2.1, I.2.2, I.2.4, I.2.6, I.2.7, I.7, L.3.2