Abstract
Massive social event relevant messages are generated in online social media, which makes the filtering and screening a great challenge. In order to obtain massages with high quality, a high quality information extraction framework based on kernel principal component analysis and wavelet transformation (KPCA-WT) is proposed. First, based on multiple features fusion, we design an algorithm to extract the microblogs of high quality, which transforms the features into wavelet domain to capture the detailed differences between the feature signals. Then the weights of the features are evaluated by EM algorithm and fused further to get a comprehensive value of each message. In addition, to reduce the effect of noisy features and speed up the operation, these features are processed through kernel principal component analysis before transforming into wavelet domain. Experimental results show that the proposed framework can extract information with higher quality, less redundancy, and greatly reduce the time consumption.
This research is supported by the Natural Science Foundation of China under contract No. 61472291, and Natural Science Foundation of Hubei Province, China under contract No. ZRY2014000901.
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Peng, M. et al. (2016). KPCA-WT: An Efficient Framework for High Quality Microblog Extraction in Time-Frequency Domain. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_24
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DOI: https://doi.org/10.1007/978-3-319-39958-4_24
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