Mapping Twitter Data of Customers' Sentiment in The Shape of Heat Map

Tight competition among companies has been making many companies to increase their efforts to collect information from various media for the sake of their service analysis. Recorded data in social media such as Twitter can be used as a potential data to be analyzed, since Twitter is becoming one of the most popular social media in the world. Text classification is developed to observe positive and negative opinions of the customers on the services given by company in the form of heat map. Prior to developing the text classifier, data preprocessing was performed in order to determine attributes, to choose data accompanying with their associated coordinates, and to decide which words are considered as positive or negative opinion. Using coordinates of data that have been classified, a heat map was used to visualize density level gradation of positive and negative opinions. The use of heat map to determine customers¿ coordinate positions whose opinions have been classified will ease company to observe customers¿ opinions density level based on their color gradation visualization that are presented in certain radius from one coordinate observing point specified by the company.