Abstract
New logining word/terms are reasonably extracted to improve the accuracy of information retrieval and to provide the theoretical support for tracking network topics and social public opinion. To efficiently mine new logining word/terms from micro-blog, this paper presents an extraction method of new logining word/term for social media based on statistics and N-increment. First, some words with very high frequency occurring in micro-blog text, called as similar stop-words, are collected into a list according to abuttal entropy. Second, micro-blog text is preprocessed by deleting similar stop-words and non-text elements. And then the preprocessed micro-blog text is scanned to extract new logining word/term according to N-increment and internal statistics. The experimental results show that the proposed algorithm can accurately and quickly extract micro-blog new logining word/term.
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