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2018 | OriginalPaper | Chapter

Topic Modeling on Online News Extraction

Authors : Aashka Sahni, Sushila Palwe

Published in: Intelligent Computing and Information and Communication

Publisher: Springer Singapore

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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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Metadata
Title
Topic Modeling on Online News Extraction
Authors
Aashka Sahni
Sushila Palwe
Copyright Year
2018
Publisher
Springer Singapore
Book
Intelligent Computing and Information and Communication

Print ISBN: 978-981-10-7244-4

Electronic ISBN: 978-981-10-7245-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7245-1_60

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