@article{scholars2997,
          volume = {4},
            note = {cited By 1},
          number = {7},
            year = {2012},
           title = {Identifying product features from customer reviews using lexical concordance},
         journal = {Research Journal of Applied Sciences, Engineering and Technology},
           pages = {833--839},
        abstract = {Automatic extraction of features from unstructured text is one of the challenging problems of Opinion Mining. The trend of getting products and services reputation from online resources such as web blogs and customer feedback is increasing day by day. Therefore efficient system is required to automatically extract products features and the opinion of consumers about all aspects of the products. In this study our focus is on extraction of product features from customer reviews. We have proposed a concordance based technique for automatic extraction of features of product from customer reviews. In our proposed technique we extract patterns of lexical terms using concordance for candidate features extraction and identify features by grouping. The proposed grouping algorithm is used to remove irrelevant features. We conducted experiments on different products reviews and compared our results with existing methods. From empirical results we proved the validity of the proposed method. {\^A}{\copyright} Maxwell Scientific Organization, 2012.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862092851&partnerID=40&md5=d1333d4c7f9bea80f7be673ec97270be},
        keywords = {Automatic extraction; Concordance; Customer feedback; Customer review; Efficient systems; Feature grouping; Features extraction; Grouping algorithm; Online resources; Opinion mining; Product feature; Products and services, Data mining; Feature extraction; Sales, Customer satisfaction},
          author = {Khan, K. and Baharudin, B. B.},
            issn = {20407459}
}