Implementation of TF-IDF Algorithm and K-mean Clustering Method to Predict Words or Topics on Twitter

Abstract

The social media time line, especially Twitter, is still interesting to follow. Various tweets delivered by the public are very informative and varied. This information should be able to be used further by utilizing the topic of conversation trends at one time. In this paper, the authors cluster the tweet data with the TF-IDF algorithm and the K-Mean method using the python programming language. The results of the tweet data clustering show predictions or possible topics of conversation that are being widely discussed by netizens. Finally, the data can be used to make decisions that utilize community sentiment towards an event through social media like Twitter.

Keywords

• data mining
• clustering
• k-mean method
• tf-idf algorithm
• twitter
• prediction