Tweets Mining for Classification and Rapid Response for Pessimistic Ones
R. Renuga1, K. Reshma2
1R. Renuga, Assistant Professor, Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai (Tamil Nadu), India.
2K. Reshma, Assistant Professor, Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai (Tamil Nadu), India.
Manuscript received on 13 December 2018 | Revised Manuscript received on 25 December 2018 | Manuscript Published on 24 January 2019 | PP: 57-60 | Volume-7 Issue-4S2 December 2018 | Retrieval Number: Es2038017519/19©BEIESP
Open Access | Editorial and Publishing 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: Social media is virtual network of communities/groups where people can create and share media /opinions/ideas on various things/ objects/ persons/ topics related to entertainment, sports, politics, science, travel etc. Twitter is the most popular micro-blogging site, which provides access to the data for non-commercial and research purpose. Mining topics in Twitter is increasingly attracting more attention. Our aim is to automate the process of responding to pessimistic tweets in feedback given to electronic products. In this way customer will be satisfied by quick response from company and retailer will get some time to work on the customer problem. So CustomerRetailer relationship can be improved. In this paper we present how Open source social media intelligence (OSSMInt) can be applied on twitter tweets to extract necessary information from the feedback that has been tweeted by customers of a particular product and quick response to those customers who are dissatisfied with product. Here we concentrate on negative feedback and satisfy the customer temporarily by allowing the retailer to work on the product. We use necessary algorithms, tools and techniques to get the data from twitter, classify it and reply the customer with quick response who has given negative feedback. This classification can be shared with a data scientist for further procedure to get a solution to the problem escalated by the customer. This works as a connecting bridge between a customer and retailer.
Keywords: Mining Classification Social Virtual Network Algorithms.
Scope of the Article: Data Mining