A method for extracting people's interest tags based on social networks

Abstract

The invention discloses a method for extracting interest tags of people based on social networks, which includes the following steps: Step A: data preprocessing, which is used to clean, screen and replace the social network data of people to form a set including multiple words; Step B: Derivation of candidate tags, read in and judge the words in the set in turn, and form a candidate tag set including topic tags, word candidate tags and phrase candidate tags; Step C: Extraction of interest tags, including candidate tags Determination of TF value; calculation of candidate tag IDF value; sorting according to the TFIDF value of candidate tags, exporting some topic tags to the interest tag set; weight calculation between candidate tags; score calculation of candidate tags; acquisition of interest tag set . The present invention has at least the following advantages: it not only considers the frequency of the interest tags in the document library and documents, but also considers the impact of the document structure on the interest tags, and can obtain more accurate effects.

Description

technical field [0001] The invention relates to the technical field of tag extraction, in particular to a method for extracting tags of people's interests based on social networks. Background technique [0002] With the rapid development of Internet applications, social networks have an increasing influence on users. People are increasingly relying on social networks for information exchange and sharing, which has brought about an explosive growth of Internet data. At the same time, users' demand for personalization is becoming stronger and stronger, such as recommending users' favorite products, games, music, movies or News and more. Character interest tags are usually used to describe the identity attributes and interest attributes of characters, which are very helpful for character retrieval and recommendation, character behavior analysis, discovery of character hobbies and character portrait models. [0003] Commonly used interest tag extraction technologies include TF...

AI Technical Summary

Problems solved by technology

[0005] Most of the existing interest tag extraction algorithms use single words as interest tags, while ignoring phrases and unique topic tags in social networks

In addition, the TFIDF algorithm mentioned above only considers the frequency of words in documents and document libraries, but does not consider the text structure of documents.

On the contrary, the TextRank algorithm only considers the role of candidate words in the document structure, but ignores the representation of candidate words in the entire corpus, which is easily affected by meaningless words (such as stop words, etc.)

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