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.
شادي إبراهيم أبوضلفة | Shadi Abudalfa | 5705