检索结果-南通市图书馆

topicS AND TRENDS OF THE ON-LINE PUBLIC CONCERNS BASED ON TIANYA FORUM

Journal of Systems Science and Systems Engineering 2014年第2期23卷 212-230页

作者： Lina Cao Xijin Tang Academy of Mathematics and Systems Science Chinese Academy of Sciences

Many social events spread fast through the Internet and arouse wide community discussions. Those on-line public opinions emerge into diverse topics along the time. Moreover, the strength of the topics is fluctuating. How to catch both primary topics and trend of topics over the shifting on-line discussions are not only of theoretical importance for scientific research, but also of practical importance for societal management especially in current China. To try the cutting-edge text analytic technologies to deal with unstructured on-line public opinions and provide support for social problem-solving in the big data era is worth an endeavour. This paper applies dynamic topic model （DTM） to explore the changing topics of new posts collected from Tianya Zatan Board of Tianya Club, the most influential Chinese BBS in China's Mainland. By analysis of the hot and cold terms trends, we catch the topics shift of main on-line concerns with illustrations of topics of school bus and environment in December of 2011. An algorithm is proposed to compute the strength fluctuation of each topic. With visualized analysis of the respective main topics in several months of 2012, some patterns of the topics fluctuation on the board are summarized.

关键词： topic models dynamic topic model on-line topics evolution Tianya Club societal management

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An enhanced cosine-based visual technique for the robust tweets data clustering

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International Journal of Intelligent Computing and Cybernetics 2021年第2期14卷 170-184页

作者： Narasimhulu K. Meena Abarna K.T. Sivakumar B. Annamalai University ChidambaramIndiaand Rajeev Gandhi Memorial College of Engineering and Technology NandyalIndia

Purpose-The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents,which is useful for achieving the robust tweets data clustering ***/methodology/approach-Let“N”be the number of tweets documents for the topics *** texts,punctuations and other symbols are removed,tokenization and stemming operations are performed in the initial tweets pre-processing ***-of-features are determined for the tweets;later tweets are modelled with the obtained bag-of-features during the process of topics *** of topics features are extracted for every tweet *** set of topics features of N documents are treated as *** key idea of the proposed work is to use multi-viewpoints in the similarity features *** following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents(here N 55)and corresponding documents are defined in projected space with five viewpoints,say,v_(1),v_(2),v_(3),v4,and *** example,similarity features between two documents(viewpoints v_(1),and v_(2))are computed concerning the other three multi-viewpoints(v_(3),v4,and v5),unlike a single viewpoint in traditional cosine ***-Healthcare problems with tweets *** models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding term frequency and inverse document frequency(TF-IDF)for unlabelled ***/value-topic models play a crucial role in the classification of health-related tweets with finding topics(or health clusters)instead of finding TF-IDF for unlabelled tweets.

关键词： Tweets data clustering topic models TF-IDF Similarity features Visual technique VAT cVAT MVCS-VAT

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