Network text analysis: A two-way classification approach |
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| Institution: | 1. Department of Enterprise Engineering, Tor Vergata University of Rome, Italy;2. School of Social Sciences, University of Manchester, United Kingdom;1. Room 1103, Bldg 24, ANU College of Business and Economics, Copland Building 24, The Australian National University, ACT, 2601, Australia;2. Dean, College of Business, Zayed University, Abu Dhabi, Khalifa City, FF1-2-051, United Arab Emirates;3. Chair of Marketing and Entrepreneurship, College of Business, Zayed University, Abu Dhabi, Khalifa City, FF1-2-049, United Arab Emirates;1. Department of Industrial Systems and Engineering, Indian Institute of Technology-Kharagpur, Kharagpur, 721302, India;2. Reliability Engineering Centre, Indian Institute of Technology-Kharagpur, Kharagpur 721302, India;1. Department of Convergence IT Engineering, Pohang University of Science and Technology, Republic of Korea;2. Department of Electrical Engineering, Pohang University of Science and Technology, Republic of Korea;3. Department of Industrial and Management Engineering, Pohang University of Science and Technology, Republic of Korea;1. School of Management, Fudan University, Shanghai, 200433, China;2. School of Economics and Management, Tsinghua University, Beijing, 100084, China;1. College of Business, Korea Advanced Institute of Science and Technology, 85 Hoegiro, Dongdaemoongu, Seoul, 02455, Republic of Korea;2. School of Industrial Management, Korea University of Technology and Education (KoreaTech), 1600, Chungjeol-ro, Byeongcheon-myeon, Dongnam-gu, Cheonan-si, Chungcheongnam-do, 31253, Republic of Korea |
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| Abstract: | Text clustering is a well-known method for information retrieval and numerous methods for classifying words, documents or both together have been proposed. Frequently, textual data are encoded using vector models so the corpus is transformed in to a matrix of terms by documents; using this representation text clustering generates groups of similar objects on the basis of the presence/absence of the words in the documents. An alternative way to work on texts is to represent them as a network where nodes are entities connected by the presence and distribution of the words in the documents. In this work, after summarising the state of the art of text clustering we will present a new network approach to textual data. We undertake text co-clustering using methods developed for social network analysis. Several experimental results will be presented to demonstrate the validity of the approach and the advantages of this technique compared to existing methods. |
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| Keywords: | Network text analysis Co-clustering Text mining |
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