Abstract
News media includes print media, broadcast news, and Internet (online newspapers, news blogs, etc.). The proposed system intends to collect news data from such diverse sources, capture the varied perceptions, summarize, and present the news. It involves identifying topic from real-time news extractions, then perform clustering of the news documents based on the topics. Previous approaches, like LDA, identify topics efficiently for long news texts, however, fail to do so in case of short news texts. In short news texts, the issues of acute sparsity and irregularity are prevalent. In this paper, we present a solution for topic modeling, i.e, a word co-occurrence network-based model named WNTM, which works for both long and short news by overcoming its shortcomings. It effectively works without wasting much time and space complexity. Further, we intend to create a news recommendation system, which would recommend news to the user according to user preference.
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References
Xueqi Cheng; Xiaohui Yan; Yanyan Lan; Jiafeng Guo. BTM: Topic Modeling over Short Texts. IEEE Transactions on Knowledge and Data Engineering, Year: 2014, Volume: 26, Issue: 12 Pages: 2928–2941.
M. Sahami and T. Heilman. A web-based kernel function for measuring the similarity of short text snippets. In Proceedings of the 15th international conference on World Wide Web, pages 377–386. ACM, 2006.
X. Phan, L. Nguyen, and S. Horiguchi. Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In Proceedings of the 17th international conference on World WideWeb, pages 91–100. ACM, 2008.
O. Jin, N. Liu, K. Zhao, Y. Yu, and Q. Yang. Transferring topical knowledge from auxiliary long texts for short text clustering. In Proceedings of the 20th ACM international conference on Information and knowledge management, pages 775–784. ACM, 2011.
D. Ramage, S. Dumais, and D. Liebling. Characterizing microblogs with topic models. In International AAAI Conference on Weblogs and Social Media, volume 5, pages 130–137, 2010.
W. Zhao, J. Jiang, J. Weng, J. He, E. Lim, H. Yan, and X. Li. Comparing twitter and traditional media using topic models. Advances in Information Retrieval, pages 338–349, 2011.
Y. Wang, E. Agichtein, and M. Benzi. Tm-lda: efficient online modeling of latent topic transitions in social media. In Proceedings of the 18th ACMSIGKDD, pages 123–131, New York, NY, USA, 2012.ACM.
Pramod Bide, Rajashree Shedge, “Improved Document Clustering using K-means Algorithm” IEEE, 2015.
Mushfeq-Us-Saleheen Shameem, Raihana Ferdous, “An efficient Kmeans Algorithm integrated with Jaccard Distance Measure for Document Clustering”, pp 1–6, IEEE, 2009.
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Sahni, A., Palwe, S. (2018). Topic Modeling on Online News Extraction. In: Bhalla, S., Bhateja, V., Chandavale, A., Hiwale, A., Satapathy, S. (eds) Intelligent Computing and Information and Communication. Advances in Intelligent Systems and Computing, vol 673. Springer, Singapore. https://doi.org/10.1007/978-981-10-7245-1_60
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DOI: https://doi.org/10.1007/978-981-10-7245-1_60
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