Trending topic discovery of Twitter Tweets using clustering and topic modeling algorithms

Abstract

There is no previous research that compares the results of k-means, CLOPE clustering and Latent Dirichlet Allocation (LDA) topic modeling algorithms for detecting trending topics on tweets. Since not all tweets contain hashtags, we considered three training data feature sets: hashtags, keywords and keywords + hashtags in this study. Our proposed methodology proved that CLOPE can also be used in a non-transactional database like Twitter data set to answer the trending topic discovery and could provide more topic patterns than k-means and LDA. Using additional feature sets has improved the results of k-means and LDA, thus, keywords + hashtags can identify more meaningful topics.