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|Title||Feature Based Approach in Arabic Opinion Mining Using Ontology|
|Title in Arabic||التنقيب عن الاراء العربية باستخدام الانتولوجيا بالاعتماد على المستوى|
With the rapid increase in the volume of Arabic reviews that use applications such as online review sites, blogs, forums, social networking, and so forth, comes at an increasing demand for Arabic opinion mining techniques. In Arabic language, researchs in this area is progressing at a very slow pace compared to that being carried out in English and other languages. In this thesis, we highlight two problems for Arabic opinion mining technique: firstly, when analyzing review having different features with diverse opinion strengths. It considers all features extracted from the reviews to be equally important in failing to determine the proper polarity of the review and makes the review’s sentiment classification less accurate. Secondly, the opinion summary for each feature doesn’t consider the sub-features that represented it and makes the featurebased summary is incomplete. This research presents a technique using ontology that work at feature level classification to classify Arabic user generated reviews by identifying the important features from the review based on level of these features on the ontology tree and to generate an opinion summary for each feature in the whole corpus by identifiying the opinion of its sub-feature terms in the ontology. To evaluate our work, we use public datasets which are hotels and books datasets. We use accuracy, recall, precision, f-measure metrics to evaluate the performance and compare the results with other supervised or unsupervised techniques. Also, subjective evaluation is used in our method to demonstrate the effectiveness of feature and opinion extraction process and summarization. We show that our method improves the performance compared with other opinion mining classification techniques, obtaining 78.83% f-measure in hotel domain and 79.18% in book domain. Furthermore, subjective evaluation shows the effectiveness of our method by obtaining an average f-measure of 84.62% in hotel domain and 86.31% in book domain.
|Publisher||الجامعة الإسلامية - غزة|
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