Ontology Learning on Product Reviews to Extract Aspects and Opinions
Sunil Bhutada1, B.Shivani2, C. Sweety3, E. Nikhil Bhargava4, K.Dhanvi5
1Sunil Bhutada, Professor, Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad (Telangana), India.
2B.Shivani, Student, Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad (Telangana), India.
3C.Sweety, Student, Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad (Telangana), India.
4E.Nikhil Bhargava, Student, Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad (Telangana), India.
5K.Dhanvi, Student, Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad (Telangana), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 360-366 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7122068519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: More number of online product reviews, into e-commerce database, from time to time on a daily basis are produced. In order to analyze huge number of reviews for aspects and opinions is a complex task. This is because these reviews that are produced from time to time are not properly structured and there is a lot of fancying in the literature. This often makes the language, unstructerd and thus makes it difficult to analyze. ‘NLP’, which means the Natural language processing and Ontology learning techniques are used to automate these tasks. The semantic gap (gap between written reviews and the actual knowledge) was observed when aspects and opinions are extracted through these techniques. The original Ontology learning (OL) reduces this gap. Maximum number of aspects and opinions extraction is estimated using OL. These aspects and opinions can be found individually or in pairs.
Keywords: Association Rule Mining, Syntactic Analysis, Apiori Algorithm, Combination Approach, Resource Description Framework, Lexico Syntactic Pattern, Natural Language Processing(NLP).
Scope of the Article: Language Processing