Short Text Clustering Using Generalized Dirichlet Multinomial Mixture Model

2021 ◽  
pp. 149-161
Author(s):  
Samar Hannachi ◽  
Fatma Najar ◽  
Nizar Bouguila
Keyword(s):  
Mixture Model ◽  
Text Clustering ◽  
Short Text ◽  
Short Text Clustering ◽  
Generalized Dirichlet

Short text clustering based on Pitman-Yor process mixture model

2017 ◽  
Vol 48 (7) ◽  
pp. 1802-1812 ◽  
Author(s):  
Jipeng Qiang ◽  
Yun Li ◽  
Yunhao Yuan ◽  
Xindong Wu
Keyword(s):  
Mixture Model ◽  
Text Clustering ◽  
Short Text ◽  
Short Text Clustering

Short-Text Clustering using Statistical Semantics

2015 ◽  
Author(s):  
Sepideh Seifzadeh ◽  
Ahmed K. Farahat ◽  
Mohamed S. Kamel ◽  
Fakhri Karray
Keyword(s):  
Text Clustering ◽  
Short Text ◽  
Short Text Clustering

Short Text Clustering Algorithms for Weibo Topic Detection

2014 ◽  
Vol 971-973 ◽  
pp. 1747-1751 ◽  
Author(s):  
Lei Zhang ◽  
Hai Qiang Chen ◽  
Wei Jie Li ◽  
Yan Zhao Liu ◽  
Run Pu Wu
Keyword(s):  
Text Analysis ◽  
Semantic Information ◽  
Clustering Algorithms ◽  
Text Clustering ◽  
Massive Data ◽  
Topic Detection ◽  
Clustering Methods ◽  
Short Text ◽  
Short Text Clustering ◽  
Application Requirements

Text clustering is a popular research topic in the field of text mining, and now there are a lot of text clustering methods catering to different application requirements. Currently, Weibo data acquisition is through the API provided by big microblogging platforms. In this essay, we will discuss the algorithm of extracting popular topics posted by Weibo users by text clustering after massive data collection. Due to the fact that traditional text analysis may not be applicable to short texts used in Weibo, text clustering shall be carried out through combining multiple posts into long texts, based on their features (forwards, comments and followers, etc.). Either frequency-based or density-based short text clustering can deliver in most cases. The former is applicable to find hot topics from large Weibo short texts, and the latter is applicable to find abnormal contents. Both the two methods use semantic information to improve the accuracy of clustering. Besides, they improve the performance of clustering through the parallelism.

Short Text Clustering Algorithm with Feature Keyword Expansion

2012 ◽  
Vol 532-533 ◽  
pp. 1716-1720 ◽  
Author(s):  
Chun Xia Jin ◽  
Hai Yan Zhou ◽  
Qiu Chan Bai
Keyword(s):  
Clustering Algorithm ◽  
Text Clustering ◽  
Experimental Results ◽  
Semantic Features ◽  
Short Text ◽  
Clustering Quality ◽  
Short Text Clustering

To solve the problem of sparse keywords and similarity drift in short text segments, this paper proposes short text clustering algorithm with feature keyword expansion (STCAFKE). The method can realize short text clustering by expanding feature keyword based on HowNet and combining K-means algorithm and density algorithm. It may add the number of text keyword with feature keyword expansion and increase text semantic features to realize short text clustering. Experimental results show that this algorithm has increased the short text clustering quality on precision and recall.

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