Method and device for mining hot-spot words

Abstract

The invention discloses a method and a device for mining hot-spot words. The method includes acquiring input text streams; subjecting the text streams to word segmentation to obtain a candidate word set; accounting a current frequency that each candidate word in the candidate word set appears in the text streams to acquire each history frequency of each candidate word in prestored history data; and calculating a frequency abnormity value of the candidate word according to the current frequency and each history frequency of the candidate word, storing a current frequency message of the candidate word in the history data, and outputting a preset number of candidate words with abnormal frequencies. By means of the method and the device for mining the hot-spot words, mining ranges of the hot-spot words can be extended, and mining efficiencies of the hot-spot words can be improved.

Description

technical field [0001] The invention relates to computer communication technology, in particular to a method and device for mining hot words. Background technique [0002] With the development of computer communication technology, especially the development of 3G network and intelligent mobile terminals, users' network life is becoming more and more abundant, such as chatting, browsing news, watching movies, playing games, searching, shopping, and publishing information on the Internet. It is increasingly becoming a part of online life. For example, microblog (MicroBlog), that is, as a platform for information sharing, dissemination and acquisition based on user relationships, users can form personal communities through WEB, WAP and various clients, update information with about 140 characters, and Realize instant sharing. [0003] Due to the abundance of network content, it takes more and more time for network users to obtain relevant information. In order to improve the ...

AI Technical Summary

Problems solved by technology

[0014] It can be seen from the above that the existing methods for mining hot words need to manually organize the hot word vocabulary, which is a heavy workload; at the same time, a large number of newly emerging names, place names, and organization names may be unregistered words, that is, they have not been sorted into hot words However, these words are often the main part of a hot event or topic, so that the mining scope of the hot word vocabulary based on manual sorting is small, and such hot events or topics cannot be mined, which makes hot word mining more efficient. Furthermore, many hot words, such as Beijing, movie, gossip, etc., are often words with high frequency. Since this word will be included in multiple events, especially on the Weibo platform, it is very likely that netizens will Entrainment of words such as Beijing and gossip makes these words mentioned or appear frequently, but the frequent occurrence of the word does not reflect a hot event or topic, that is to say, it is not possible to rely solely on the frequency of occurrence of words within a certain period of time. It really reflects the popularity of the word; moreover, the output hot word is a single word, and in an environment that lacks context, it is difficult for a single word to reflect a hot event or topic. For example, for the case where the output hot word is Cote d’Ivoire, in In the absence of relevant knowledge background, it is difficult for users to understand which hot event or topic this word represents

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