Real-time news content-oriented stream topic evolution tracking method

Abstract

The invention discloses a real-time news content-oriented stream topic evolution tracking method. The method comprises the steps of firstly, batching news contents collected in real time according totime periods, and mining a preliminary topic result for each batch of the news contents by adopting an LDA method; secondly, performing named entity identification in the batch of the news contents, and calculating correlation between topics and entities, thereby updating entity link relationships in an entity library; thirdly, through topic inner lexical item clustering, obtaining a topic-topic inner class cluster corresponding relationship, and storing topic results in a topic library; and finally, calculating popularity information of the topics and topic inner class clusters, and accordingto the popularity information, performing dynamic updating on LDA topic mining parameters for topic evolution tracking of the next batch of the news contents. According to the method, topic featuresand class cluster features of topic inner lexical items in the real-time news contents can be mined; difference among the topics and among different class clusters in the topics is fully utilized; andthe LDA topic mining parameters are dynamically updated.

Description

technical field [0001] The present invention relates to a streaming topic evolution tracking method for real-time news content. The method can conduct time-by-segment, streaming topic mining and evolution tracking of news content collected in real time from the Internet by means of dynamic update of topic mining parameters. , belonging to the field of Internet and natural language processing technology. Background technique [0002] In recent years, with the vigorous development of information technology, the Internet has become the most convenient channel for people to obtain information and understand news. However, while the Internet news information resources are extremely rich, it also brings the huge challenge of "information overload", that is, the continuous emergence of massive news content makes it difficult for users to obtain useful parts from them, which in turn affects the quality of news content. Effective utilization. Personalized recommendation technology ...

AI Technical Summary

Problems solved by technology

However, the traditional LDA method cannot measure the relationship between the results of topic mining before and after, and the OLDA method cannot make full use of the differences between topics and between topics in time when describing the relationship between the results of topic mining before and after. Accuracy is lacking

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