01603nas a2200145   4500008004100000245007900041210006900120260001200189300001100201490000700212520114600219100001801365700001801383856005601401       2020                        eng d00aA Graph-Based Approach for Aspect Extraction from Online Customer Reviews-0 aGraphBased Approach for Aspect Extraction from Online Customer R  c06/2022  a99-1080 v183 a<p><span style="color: rgb(51, 51, 51); font-family: Arial, Helvetica, sans-serif;">&nbsp;E-commerce websites have become main market players in the 21st century due to advancement in the internet technology. Apart from buying products online, customers are also providing reviews on the products purchased by them. These reviews help new customers to buy various products according to their needs, liking, and preferences. However, millions of reviews are added by the customer on a daily basis. To extract meaningful information manually from these huge amounts of reviews is a tough task. So, it is required to develop an automatic analytics tool for the review sentences. Aspect extraction is one of the vital tasks in the process of meaningful information extraction from the products having various entities. In this work, a novel product aspect extraction approach has been proposed which utilize a graphbased technique with the integration of statistical and semantic information. The analysis of experimental results shows that the proposed approach is efficient and effective in comparison to the state of art methods.</span></p>
1 aKumar, Rakesh1 aSharan, Aditi  uhttp://dline.info/fpaper/jdim/v18i3/jdimv18i3_2.pdf00439nas a2200133   4500008004100000245009500041210006900136260001200205300000900217490000600226100001800232700001800250856003700268       2018                        eng d00aHybrid Multi-Criteria Decision Making Approach for Product Ranking Using Customers Reviews0 aHybrid MultiCriteria Decision Making Approach for Product Rankin  c03/2018  a1-170 v81 aKumar, Rakesh1 aSharan, Aditi  uhttps://dline.info/newl/node/648