Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Flexible Ontology Reasoning Architecture for the Semantic Web
IEEE Transactions on Knowledge and Data Engineering
Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling
IEEE Transactions on Knowledge and Data Engineering
To Determine the Weight in a Weighted Sum Method for Domain-Specific Keyword Extraction
ICCET '09 Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 01
Social Network Analysis of Network Communities
ICMB '09 Proceedings of the 2009 Eighth International Conference on Mobile Business
Unsupervised approaches for automatic keyword extraction using meeting transcripts
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A New Web Service Evaluation Model with Fuzzy C-Means Artificial Immune Network Memory Classifier
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 02
Ontology-Based Context Representation and Reasoning Using OWL and SWRL
CNSR '10 Proceedings of the 2010 8th Annual Communication Networks and Services Research Conference
Semi-automatic hot event detection
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Topics in professional blogs mainly refer to specific techniques. Today, professional blog websites have been important information sources. However, information overload and the uncertainty of topic hotness evaluation have been obstacles for hot topic detection. The paper proposes a method of detecting hot topics in professional blogs. The proposed method is based on the characteristics of the professional blogs and mainly analyzes candidate topics that are likely to be hot. First, a word network based on high frequency keywords and co-occurrences of the keywords is constructed, and then the candidate topics are extracted by analyzing the structure of the word network. The opinion networks with respect to the topics in different time intervals are subsequently constructed for opinion analysis. Finally, hot topics are identified by computing the user participation degree, opinion communication degree, and timeliness of the candidate topics. Experimental results show the proposed method is feasible and reasonable.