TY  - JOUR
T1  - Feature Transfer Through New Statistical Association
Measure for Cross-Domain Sentiment Analysis
AU - Al-Moslmi, Tareq AU - Al-Shabi, Adel AU - Albared, Mohammed AU - Omar, Nazlia 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 1
SP  - 164
EP  - 170
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.164.170
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.164.170
KW  - co-occurrence calculation methods
KW  -cross-domain sentiment analysis
KW  -Sentiment analysis
KW  -sentiment thesaurus
KW  -Malaysia
AB  - 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.
ER  -