TY  - JOUR
N1  - cited By 22
IS  - 3
SP  - 281
ID  - scholars5386
TI  - Identifying product features from customer reviews using hybrid patterns
SN  - 16833198
Y1  - 2014///
A1  - Khan, K.
A1  - Baharudin, B.
A1  - Khan, A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900015678&partnerID=40&md5=010658ce2b6361f696dcce1a34884b4f
PB  - Zarka Private Univ
EP  - 286
AV  - none
N2  - In this paper we have addressed the problem of automatic identification of product features from customer reviews. Costumers, retailors, and manufacturers are popularly using customer reviews on websites for product reputation and sales forecasting. Opinion mining application have been potentially employed to summarize the huge collectionof customer reviews for decision making. In this paper we have proposed hybrid dependency patterns to extract product features from unstructured reviews. The proposed dependency patterns exploit lexical relations and opinion context to identify features. Based on empirical analysis, we found that the proposed hybrid patterns provide comparatively more accurate results. The average precision and recall are significantly improved with hybrid patterns.
JF  - International Arab Journal of Information Technology
VL  - 11
ER  -