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Enriching short text representation in microblog for clustering

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Abstract

Social media websites allow users to exchange short texts such as tweets via microblogs and user status in friendship networks. Their limited length, pervasive abbreviations, and coined acronyms and words exacerbate the problems of synonymy and polysemy, and bring about new challenges to data mining applications such as text clustering and classification. To address these issues, we dissect some potential causes and devise an efficient approach that enriches data representation by employing machine translation to increase the number of features from different languages. Then we propose a novel framework which performs multi-language knowledge integration and feature reduction simultaneously through matrix factorization techniques. The proposed approach is evaluated extensively in terms of effectiveness on two social media datasets from Facebook and Twitter. With its significant performance improvement, we further investigate potential factors that contribute to the improved performance.

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Authors and Affiliations

  1. Computer Science & Engineering, Arizona State University, Tempe, AZ, 85281, USA

    Jiliang Tang, Xufei Wang, Huiji Gao, Xia Hu & Huan Liu

Authors
  1. Jiliang Tang
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  2. Xufei Wang
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  3. Huiji Gao
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  4. Xia Hu
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  5. Huan Liu
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Corresponding author

Correspondence to Jiliang Tang.

Additional information

Jiliang Tang is a PhD student in computer science and engineering at Arizona State University. He received his BSc and MSc degrees from Beijing Institute of Technology in 2008 and 2010. His research interests include data mining and machine learning. Specifically, he is interested in social computing and feature selection in social media.

Xufei Wang is a PhD student in computer science and engineering at Arizona State University. He received his Masters degree from Tsinghua University, and Bachelor degree of Science from Zhejiang University, China. His research interests are in social computing and data mining. Specifically, he is interested in mining social media data, social network analysis, mining ego-centric friend structure, tag network, crowdsourcing, etc. He is an IEEE student member.

Huiji Gao is a PhD student in Data Mining and Machine Learning (DMML) Lab at Arizona State University (ASU). He received his BSc and MSc degrees from Beijing University of Posts and Telecommunications, China in 2007 and 2010. His research interests include social computing, data mining, and social media mining, in particular, crowdsourcing and spatial-temporal mining. Contact him at huiji.gao@asu.edu.

Xia Hu is a PhD student of Computer Science and Engineering at Arizona State University. He received his Master and Bachelor degrees from the School of Computer Science and Engineering of Beihang University. His research interests are in text analytics in social media, social network analysis, machine learning, text representation, sentiment analysis, etc. He was awarded an ASU GPSA Travel Grant, Machine Learning Summer School at Purdue Fellowship, SDM Doctoral Student Forum Fellowship, and various Student Travel Awards and Scholarships from ASU, NUS, and BUAA.

Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his PhD degree in Computer Science at the University of Southern California and BEng degree in Computer Science and Electrical Engineering at Shanghai Jiao Tong University. His research focus is centered on investigating problems that arise in many realworld applications with high-dimensional data of disparate forms such as analyzing social media, group interaction and modeling, feature selection, and text/web mining. His wellcited publications include books, book chapters, encyclopedia entries as well as conference and journal papers.

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Tang, J., Wang, X., Gao, H. et al. Enriching short text representation in microblog for clustering. Front. Comput. Sci. 6, 88–101 (2012). https://doi.org/10.1007/s11704-011-1167-7

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  • DOI: https://doi.org/10.1007/s11704-011-1167-7

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