DFKI-LT - Hashtag Processing for Enhanced Clustering of Tweets

Dagmar Gromann, Thierry Declerck
Hashtag Processing for Enhanced Clustering of Tweets
in: Galia Angelova, Kalina Bontcheva, Ruslan Mitkov, Ivelina Nikolova, Irina Temnikova (eds.):
1 Proceedings of the INTERNATIONAL CONFERENCE RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING 2017, Varna, Bulgaria, INCOMA Ltd, University of Wolverhampton and Bulgarian Academy of Sciences, Shoumen, Bulgaria, 9/2017
 
Rich data provided by tweets have been analyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering.Tweets and hashtags are generally analyzed on sentential or word level but not on a compositional level of concatenated words. We propose an approach for a closer analysis of compounds in hashtags, and in the long run also of other types of text sequences in tweets, in order to enhance the clustering of such text documents. Hashtags have been used before as primary topic indicators to cluster tweets, however, their segmentation and its effect on clustering results have not been investigated to the best of our knowledge. Our results with a standard dataset from the Text REtrieval Conference (TREC) show that segmented and harmonized hashtags positively impact effective clustering.
 
Files: BibTeX, ranlp2017-graph-enhanced.pdf