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.

Promoting organizers such as marketing planners frequently utilize geographical information systems to single out appropriate retail stores, setting up advertising campaigns based on regions, and target direct showcasing exercises. Geographical information systems topical maps encourage the visual evaluation of regions. A wide arrangement of elective symbolization, for example, circles, bars, or shading, can be utilized to represent quantitative geospatial data on such maps. In any case, there is little learning on which sort of symbolization is most viable for each case. In a huge scale exploratory study, the creators demonstrate that the symbolization firmly impacts choice execution. GIS-based representations encourage the evaluation of store areas and help organizers to choose the most encouraging options. The choice of the best option requires a visual streamlining which is supported by GIS topical maps. In this study, the manner in which how various GIS-based information portrayals impact advertising analyst’s decision making for online food delivery platforms is explored. The outcomes demonstrate that GIS maps are an important piece of work and that sort of guided representation impacts the initiated decision-making process. Keywords – Geographical Visualization; Food delivery platform; Online food ordering