Extractive Review Summarization Framework for Extracted Features
Palak Bansal1, Somya2, Nazar Kamaal3, Shreya Govil4, Tameem Ahmad5
1Palak Bansal, Department of Computer Engineering, Z. H. College of Engineering & Technology, Aligarh Muslim University, Aligarh, India.
2Somya, Department of Computer Engineering, Z. H. College of Engineering & Technology, Aligarh Muslim University, Aligarh, India.
3Nazar Kamaal, Department of Computer Engineering, Z. H. College of Engineering & Technology, Aligarh Muslim University, Aligarh, India.
4Shreya Govil, Department of Computer Engineering, Z. H. College of Engineering & Technology, Aligarh Muslim University, Aligarh, India.
5Tameem Ahmad, Department of Computer Engineering, Z. H. College of Engineering & Technology, Aligarh Muslim University, Aligarh, India.
Manuscript received on 15 May 2019 | Revised Manuscript received on 22 May 2019 | Manuscript Published on 10 July 2019 | PP: 434-439 | Volume-8 Issue-7C2 May 2019 | Retrieval Number: G10940587C219/19©BEIESP
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© 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: In the information age, the growth of e-commerce has brought the products’ sale and purchase online and many of the customers prefer to buy it online. To support this preference the users’ reviews of the products plays an important role. So, online merchants wish to take the reviews; experiences of the user, to enhance their business and revenue. Popular and trending products may attract large number of reviews. Further, many of which could be elongated. Extracting useful information with efficiency and accuracy from these so many reviews, of which there are some very long, is a challenging task. This work is an attempt to summarize the customer reviews on products into more useful and shorter version that can help another users’ decision. Reviews available online are crawled for product, each time after extraction, first identification of features of the product will be done and hence polarity will be detected i.e. either a review is positive review or a negative review. After the calculations, summarization of all the features of the product will be generated.
Keywords: Text Summarization, Text Mining, Opinion Mining, Extractive Summary, Abstractive Summary, Feature Identification.
Scope of the Article: Computer Networks and Inventive Communication Technologies