@article{MAKHILLJEAS201712114059,
    title = {Feature Transfer Through New Statistical Association
Measure for Cross-Domain Sentiment Analysis},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {1},
    pages = {164-170},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.164.170},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.164.170},
    author = {Tareq,Adel,Mohammed and},
    keywords = {co-occurrence calculation methods,cross-domain sentiment analysis,Sentiment analysis,sentiment thesaurus,Malaysia},
    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.}
    }