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Microblog Retrieval Using Ensemble of Feature Sets through Supervised Feature Selection

Chy, Abu Nowshed; Ullah, Md Zia; Aono, Masaki


Abu Nowshed Chy

Masaki Aono


Microblog, especially twitter, has become an integral part of our daily life for searching latest news and events information. Due to the short length characteristics of tweets and frequent use of unconventional abbreviations, content-relevance based search cannot satisfy user's information need. Recent research has shown that considering temporal and contextual aspects in this regard has improved the retrieval performance significantly. In this paper, we focus on microblog retrieval, emphasizing the alleviation of the vocabulary mismatch, and the leverage of the temporal (e.g., recency and burst nature) and contextual characteristics of tweets. To address the temporal and contextual aspect of tweets, we propose new features based on query-tweet time, word embedding, and query-tweet sentiment correlation. We also introduce some popularity features to estimate the importance of a tweet. A three-stage query expansion technique is applied to improve the relevancy of tweets. Moreover, to determine the temporal and sentiment sensitivity of a query, we introduce query type determination techniques. After supervised feature selection, we apply random forest as a feature ranking method to estimate the importance of selected features. Then, we make use of ensemble of learning to rank (L2R) framework to estimate the relevance of query-tweet pair. We conducted experiments on TREC Microblog 2011 and 2012 test collections over the TREC Tweets2011 corpus. Experimental results demonstrate the effectiveness of our method over the baseline and known related works in terms of precision at 30 (P@30), mean average precision (MAP), normalized discounted cumulative gain at 30 (NDCG@30), and R-precision (R-Prec) metrics.


Chy, A. N., Ullah, M. Z., & Aono, M. (2017). Microblog Retrieval Using Ensemble of Feature Sets through Supervised Feature Selection. IEICE Transactions on Information and Systems, 100(4), 793-806.

Journal Article Type Article
Publication Date 2017-04
Deposit Date Mar 13, 2023
Journal IEICE Transactions on Information and Systems
Print ISSN 0916-8532
Publisher Institute of Electronics, Information and Communication Engineers
Peer Reviewed Peer Reviewed
Volume 100
Issue 4
Pages 793-806
Keywords microblog search, temporal information retrieval, query expansion, feature selection, learning to rank, time-aware ranking