Edinburgh Napier University

  Microblog, especially Twitter, have become an integral part of our daily life for searching latest news and events information. Due to short length characteristics of tweets, only content-relevance based search result cannot satisfy user's information need. Recent research shows that considering temporal aspects in this regard improve the retrieval performance significantly. In this paper, we propose a method to re-rank the search result based on temporal features, account related features, and Twitter specific features along with textual features of tweets. We also applied a two stage query expansion technique to improve the relevancy of tweets. After automatic feature selection by using LASSO and elastic-net regularization; we applied random forest as a feature ranking method to estimate the importance of selected feature. Then, with that importance score, a weighted ranking model combines the features value to estimate the relevance score. We conducted our experiments based on the TREC Microblog 2011 and 2012 queries over the TREC Tweets2011 collection. Experimental result demonstrates the effectiveness of our method over the baseline in terms of precision@30 (P@30), mean average precision (MAP), and reciprocal-precision (R-Prec) metrics.