Scenic spot evaluation knowledge base construction method based on metaphor topic mining

Abstract

The invention discloses a scenic spot evaluation knowledge base construction method based on metaphor topic mining. The method comprises the steps of using a scenic spot recessive topic mining algorithm to construct a scenic spot recessive multi-topic knowledge base; S2, constructing a metaphor multi-topic knowledge base of the scenic spot by adopting a scenic spot metaphor topic feature mining algorithm; S3, constructing a scenic spot evaluation knowledge base based on the semantic matching calculation model of the scenic spot corpus, and identifying the theme to which the tourist comment data belongs and the emotional tendency corresponding to the theme based on the scenic spot evaluation knowledge base. According to the invention, the scenic spot evaluation knowledge base considering metaphor information is constructed; according to the technical scheme, the fine-grained theme of each comment and the emotional tendency information of the corresponding theme in the internet tourism website can be accurately judged, data support is provided for tourists, the tourists are assisted in making decisions conforming to the preferences of the tourists, scenic spot managers can be assisted in improving scenic spot services, and the network public praise of the scenic spots is improved.

Description

technical field [0001] The invention relates to the technical field of big data analysis, in particular to a method for constructing a scenic spot evaluation knowledge base based on metaphor theme mining. Background technique [0002] With the vigorous development of the tourism Internet, more and more tourists will use social media platforms to record their travel experience during the travel process, and truly record what they see, hear and feel during the travel process. Most of them exist in the form of text or pictures on various tourism websites or social platforms on the Internet. This information can reflect the changes of scenic spots in different subject indicators in real time, and can provide big data analysis support for tourists' travel routes and preferences. Provide assistance to the managers of scenic spots in improving the services of scenic spots. [0003] At present, researchers at home and abroad focus on the hotel industry in their research on tourism ...

AI Technical Summary

Problems solved by technology

The method based on traditional supervised learning requires a lot of manual labeling work, and based on the traditional rule-based method, the current research has not yet made general rules for the corpus in the tourism field, and the rules in other fields cannot be transplanted to the tourism field.

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