Tareq  Al-Moslmi, Adel  Al-Shabi, Mohammed  Albared, Nazlia  Omar, 
    Feature Transfer Through New Statistical Association
Measure for Cross-Domain Sentiment Analysis,
    Journal of Engineering and Applied Sciences,
    Volume 12,Issue 1,
    2017,
    Pages 164-170,
    ISSN 1816-949x,
    jeasci.2017.164.170,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2017.164.170)
    Abstract: With the outgrowth of user-based web content, individuals can freely express their opinion in many
domains. However, this would imply a huge cost to annotate training data for a large number of domains and
prevent us from exploiting the information shared across various domains. As a result, cross-domain sentiment
analysis is a challenging NLP task due to feature divergence and polarity divergence. However, to tackle this
issue, this study presents a new model for cross-domain sentiment classification. This model is based on
transferring features between source and target domains vice versa, using a Union of Conditional Probability
(UCP) association measure. A Naive Bayes (NB) classifier and three feature selection methods (Information
gain, Odd ratio, Chi-square) are used to evaluate the proposed model. Experimental results show that our
model’s results were very promising and encourages us to further pursue this research.
    Keywords: co-occurrence calculation methods;cross-domain sentiment analysis;Sentiment analysis;sentiment thesaurus;Malaysia