원문정보
초록
영어
In recent years, topic detection has become a hot research point of the social network, which can be very good to find the key factors from the massive information and thus discover the topics. The traditional label propagation-based topic discovery algorithm (LPA) is widely concerned because of its approximate linear time complexity and there is no need to define the target function. However, LPA algorithm has the uncertainty and the randomness, which affects the accuracy and the stability of the topic discovery. In this paper, a method for clustering label words based on mutual information analysis is presented to find the current topic. Firstly, through filtering the stop words and extracting keywords with TF-IDF, topic words are been extracted out, and then a common word matrix is built, a topic discovery algorithm based on mutual information and label clustering is put forward. Finally, extensive experiments on two real datasets validate the effectiveness of the proposed MI-LC (Mutual information-Label clustering) algorithm against other well-established methods LPA and LDA in terms of running time, NMI value and perplexity value.
목차
1. Introduction
2. Related Work
3. Theoretical Foundation
3.1 Mutual Information and Self Information
3.2 Information Entropy and Conditional Entropy
3.3 Average Mutual Information
3.4 Relationship between Average Mutual Information and Entropy
3.5 Constructing the Topic Time Series Relation Chain
4. The Proposed Algorithm
4.1 Measuring the Node Importance
4.2 Label Clustering of Vertices Based on K-Means Algorithm
4.3 Algorithm Implementation
5. Experimental Results and Analysis
5.1 Experimental Datasets and Experimental Environment
5.2 Evaluation Metrics
5.3 Experimental Results Analysis
6. Conclusions and Future Work
Acknowledgements
References