Journal
APPLIED SOFT COMPUTING
Volume 47, Issue -, Pages 235-250Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2016.06.003
Keywords
Opinion mining; Fuzzy domain ontology; Support Vector Machine
Categories
Funding
- Korean National Research Foundation (NRF) - Korean Government [2014R1A1A2053339]
- National Research Foundation of Korea [2014R1A1A2053339] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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With the explosion of Social media, Opinion mining has been used rapidly in recent years. However, a few studies focused on the precision rate of feature review's and opinion word's extraction. These studies do not come with any optimum mechanism of supplying required precision rate for effective opinion mining. Most of these studies are based on Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and classical ontology. These systems are still imperfect for classifying the feature reviews into more degrees of polarity terms (strong negative, negative, neutral, positive and strong positive). Further, the existing classical ontology-based systems cannot extract blurred information from reviews; thus, it provides poor results. In this regard, this paper proposes a robust classification technique for feature review's identification and semantic knowledge for opinion mining based on SVM and Fuzzy Domain Ontology (FDO). The proposed system retrieves a collection of reviews about hotel and hotel features. The SVM identifies hotel feature reviews and filter out irrelevant reviews (noises) and the FDO is then used to compute the polarity term of each feature. The amalgamation of FDO and SVM significantly increases the precision rate of review's and opinion word's extraction and accuracy of opinion mining. The FDO and intelligent prototype are developed using Protege OWL-2 (Ontology Web Language) tool and JAVA, respectively. The experimental result shows considerable performance improvement in feature review's classification and opinion mining. (C) 2016 Elsevier B.V. All rights reserved.
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