Sense-Level Semantic Clustering of Hashtags in Social Media

We enhance the accuracy of the currently available semantic hashtag clustering method, which leverages hashtag semantics extracted from dictionaries such as Wordnet and Wikipedia. While immune to the uncontrolled and often sparse usage of hashtags, the current method distinguishes hashtag semantics only at the word level. Unfortunately, a word can have multiple senses representing the exact semantics of a word, and, therefore, word-level semantic clustering fails to disambiguate the true sense-level semantics of hashtags and, as a result, may generate incorrect clusters. This paper shows how this problem can be overcome through sense-level clustering and demonstrates its impacts on clustering behavior and accuracy.