@article {780,
	title = {A Graph-Based Approach for Aspect Extraction from Online Customer Reviews-},
	journal = {Journal of Digital Information Management},
	volume = {18},
	year = {2020},
	month = {06/2022},
	pages = {99-108},
	type = {Research},
	chapter = {99},
	abstract = {<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>
},
	doi = {https://doi.org/10.6025/jdim/2020/18/3/99-108},
	url = {http://dline.info/fpaper/jdim/v18i3/jdimv18i3_2.pdf},
	author = {Rakesh Kumar and Aditi Sharan}
}
@article {648,
	title = {Hybrid Multi-Criteria Decision Making Approach for Product Ranking Using Customers Reviews},
	journal = {Journal of Data Processing},
	volume = {8},
	year = {2018},
	month = {03/2018},
	pages = {1-17},
	type = {Research},
	author = {Rakesh Kumar and Aditi Sharan}
}
