Using Hashtag Graph without Co-Occurrence in Microblogs and Connect Semantically-Related Words

Abstract

With billions of active users, Twitter is popular because of its massive spreading of instant messages (i.e. tweets), bursts of world news, entertainment gossip about celebrities, and discussions over recently released products are all spreading on Twitter vividly. The shortness and informality of tweets leads to extreme sparse vector representations with a large vocabulary. This makes the conventional topic models (e.g., Latent Dirichlet Allocation and Latent Semantic Analysis) fail to learn high quality topic structures. Tweets are always showing up with rich user-generated hashtags. The hashtags make tweets semi-structured inside and semantically related to each other. Since hashtags are utilized as keywords in tweets to mark messages or to form conversations, they provide an additional path to connect semantically related words. treating tweets as semi-structured texts, we propose a novel topic model, denoted as Hashtag Graph-based Topic Model (HGTM) to discover topics of tweets. By utilizing hashtag relation information in hashtag graphs, HGTM is able to discover word semantic relations even if words are not co-occurred within a specific tweet. With this method, HGTM successfully alleviates the sparsity problem. Our investigation illustrates that the user-contributed hashtags could serve as weakly-supervised information for topic modeling, and the relation between hashtags could reveal latent semantic relation between words. We evaluate the effectiveness of HGTM on tweet (hashtag) clustering and hashtag classification problems. Experiments on two real-world tweet data sets show that HGTM has strong capability to handle sparseness and noise problem in tweets.