Customer Feedback Analyzer
Sanya Taneja1, Kartikeya Jha2, Nakul Lakhotia3, Vedanta Kapoor4, Swarnalatha P.5
1Sanya Taneja, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu) India.
2Kartikeya Jha, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu) India.
3Nakul Lakhotia, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu) India.
4Vedanta Kapoor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu) India.
5Swarnalatha P., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu) India.
Manuscript received on November 25, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1824-1827 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2664129219/2019©BEIESP | DOI: 10.35940/ijeat.B2664.129219
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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: Product reviews always act as a great source of information for a company. These reviews record the customer’s feedback on the product and services that the company provides. The problem that we face is that the number of reviews in these kinds of portals are in thousands for which manual analysis is time consuming and inefficient. So, we plan to make an automated system using machine learning which can do the job of analyzing large number of comments in seconds thereby increasing efficiency. These reviews which are posted online can be both positive and negative, categorizing them into broad categories like product defected, product size invalid, good fitting, excellent working etc., will ease the process for both consumers and sellers. This will help automate the process of Customer Resolution, as it takes a lot of time for an employee to manually sort each comment into various categories and then send it to the particular team for review. Additionally, trends can also be analyzed on these comments, such as which issue is most faced by consumers around a particular date/time. This project will efficiently extract the important topics of concern which the company should focus on and also change or improve in order to keep its customers happy and loyal.
Keywords: Customer Experience, Deep Neural Network, Natural language processing, Sentiment analysis