Abstract
With the rapid development of the online social media, microblogging events surveillance has become a major research topic. Traditional search method does not consider the characteristics of the events, the search algorithm has its limitations. To solve this problem, we proposed a microblogging event search method based on Long Short Term Memory (LSTM) networks called MESL. Using training corpus to extract the common characteristics of microblogging events. The establishment of event search model effectively improves the microblogging event search quality. Experimental results on the real microblogging datasets show that MESL model is better than the traditional methods for microblogging event search.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 61502042, No. 61320106006, No. 61532006).
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Zheng, W., Du, J., Lai, J., Liang, M., Luo, A. (2018). Microblogging Event Search Based on LSTM Model. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-6496-8_26
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DOI: https://doi.org/10.1007/978-981-10-6496-8_26
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