Abstract
With the rapid development of Internet, a large number of new words have emerged and widely been used in social network. Traditional segmentation algorithm can’t identify these new words efficiently, which will greatly affect the accuracy in extracting out these hot words and keywords. Moreover, it will affect the performance of the network public opinion monitoring system. In this paper, we use tweets collected from Twitter as the experimental data-set. By calculating frequency statistics of k-gram strings, we can find out new words as candidates, and then identify new words by their practical application frequency using Twitter’s search function. The experiment shows: this segmentation algorithm can effectively identify the new keywords and is more suitable for public opinion monitoring system.
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Xiaoyan, W., kai, X., Ying, S., Jian-long, T., Li, G. (2013). Research of New Words Identification in Social Network for Monitoring Public Opinion. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2012. Communications in Computer and Information Science, vol 320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35795-4_75
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DOI: https://doi.org/10.1007/978-3-642-35795-4_75
Publisher Name: Springer, Berlin, Heidelberg
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