Analisis Opini Terhadap Fitur Smartphone Pada Ulasan Website Berbahasa Indonesia - Doni Setyawan; Edi Winarko (IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2016)
Through online stores, consumers can give an opinion of a product, one of the best-selling products is smartphone. Their opinions become valuable and can be worthwhile to know the advantages or disadvantages of products based on the user’s experience. Therefore, in order to utilize the data of customers' opinions, it is necessary to create a system that automatically performs mining and summarizing opinions on smartphone product. The system consist of five parts: data collection, preprocessing review, feature mining, analysis of opinions and then visualize the results. Data collection is taking data reviews website using web scraping, preprocessing review is for cleaning data reviews. Feature mining stage  will find features in the reviews with apriori algorithm to produce frequent item set, then analyze the opinion using lexicon based, language rule and score function. The result will be shown in graphical form. From the testing of  feature mining obtained average recall score at 0.63 and precision at 0.72. It depends on good or bad quality of reviews. The results of testing accuracy opinion analysis shows high value with accuracy 81.76 %. The technique showed good results with opinion data which is labeled, using language rule and the implementation of score function.