Social Media Data Analytics to Improve the Customer Services: The Case of Fast-Food Companies
Rose S1, Sreejith R2, Senthil S3 
1Rose S, Rajagiri Business School, Kochi, India.
2Sreejith R, Asst. Professor, Rajagiri College of Social Sciences, Kochi, India.
3Senthil S, Director, School of CSA, REVA University, Bangalore India.

Manuscript received on 05 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 6359-6366 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2205078219/2019©BEIESP | DOI: 10.35940/ijrte.B2205.078219
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Abstract: The proposed paper exhibits a study that uses Twitter to identify and assess client perception in the fast food industry. The intention behind the research is to measure the customer discernment about the product and the services by observing and inspecting the public twitter comments, to disclose the insights on whether the customers are satisfied or not satisfied with the brand and to predict the business growth and do further improvements accordingly.The methodology in the paper incorporates the text-based analysis using Support Vector Machine (SVM), which is a classifier that helps to classify the negative and positive words. In this approach, 10,000 public tweets about the customer services of three fast-food organizations were extracted. The text-based analysis (also known as word-based analysis) was conducted using R-programming by calculating the NPS value, highlighting the most negative and positive words.From the study, the negative and positive words in the Twitter comments for McDonald’s, Pizza Hut and Burger King were extracted showing the customer perception towards these three companies. Along with this, the NPS score for each of the company was also found out.The Paper shows how text mining of tweets can be utilized in market research to help in revealing the customer perception of any product or service offered by McDonald’s, Pizza Hut and Burger King.
Index Terms: Fast-food Company, NPS (Net Promoter Score), Customer Perception, Twitter Data, Text-Mining.

Scope of the Article: Text-Mining.