NDLI: Twitter Trending Topic Classification

Content Provider	IEEE Xplore Digital Library
Author	Lee, K. Palsetia, D. Narayanan, R. Patwary, M.M.A. Agrawal, A. Choudhary, A.
Copyright Year	2011
Abstract	With the increasing popularity of microblogging sites, we are in the era of information explosion. As of June 2011, about 200 million tweets are being generated everyday. Although Twitter provides a list of most popular topics people tweet about known as Trending Topics in real time, it is often hard to understand what these trending topics are about. Therefore, it is important and necessary to classify these topics into general categories with high accuracy for better information retrieval. To address this problem, we classify Twitter Trending Topics into 18 general categories such as sports, politics, technology, etc. We experiment with 2 approaches for topic classification, (i) the well-known Bag-of-Words approach for text classification and (ii) network-based classification. In text-based classification method, we construct word vectors with trending topic definition and tweets, and the commonly used tf-idf weights are used to classify the topics using a Naive Bayes Multinomial classifier. In network-based classification method, we identify top 5 similar topics for a given topic based on the number of common influential users. The categories of the similar topics and the number of common influential users between the given topic and its similar topics are used to classify the given topic using a C5.0 decision tree learner. Experiments on a database of randomly selected 768 trending topics (over 18 classes) show that classification accuracy of up to 65% and 70% can be achieved using text-based and network-based classification modeling respectively.
Starting Page	251
Ending Page	258
File Size	1289942
Page Count	8
File Format	PDF
ISBN	9781467300056
DOI	10.1109/ICDMW.2011.171
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Social Networks Computational modeling Machine learning Topic Classification Twitter Data models Labeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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