TF-IDF method in ranking keywords of Instagram users´ image captions

Instagram is one of the popular social media applications used by a wide range of people around the world. The significant growth of active Instagram users affects the size of Instagram data. The more number of users, the larger and more various Instagram data is posted. In line with its popularity, in recent years many researchers begin to study and analyze it for various purposes, such as detecting event photos based on location, clustering the photo content, advertising strategies based on user types, and so on. As of now there are three types of data available in Instagram which are text, image, and video. In this paper we propose Term-Frequency and Inverse Document Frequency (TF-IDF) method to rank keywords of top twenty most followed Instagram users based on image captions of Instagram. The objective of this research is to automatically know the main idea of Instagram users based on 50 recent image captions posted. In our experiments, TF-IDF has been successfully implemented to reveal a set of keywords with its ranking. The highest ranking of keyword is indeed the main topic of a user, indicated by the value of TF-IDF. The result of study indicates that TF-IDF method is very useful to find and rank the keywords of Instagram users image captions. In the future research, the ranking keywords are needed in solving classification and clustering tasks as feature extractions.