Towards Semantic Retrieval of Hashtags in Microblogs

On various microblogging platforms like Twitter, the users post short text messages ranging from news and information to thoughts and daily chatter. These messages often contain keywords called Hashtags, which are semantico-syntactic constructs that enable topical classification of the microblog posts. In this poster, we propose and evaluate a novel method of semantic enrichment of microblogs for a particular type of entity search -- retrieving a ranked list of the top-k hashtags relevant to a user's query Q. Such a list can help the users track posts of their general interest. We show that our technique significantly improved microblog retrieval as well. We tested our approach on the publicly available Stanford sentiment analysis tweet corpus. We observed an improvement of more than 10% in NDCG for microblog retrieval task, and around 11% in mean average precision for hashtag retrieval task.