Research Article
Semantic Analysis of Massive Text under Multi-Model Strategy
@INPROCEEDINGS{10.4108/eai.17-6-2022.2322807, author={Zekun Tao and Youwei Zhang and Feiyue Fang and Jing Li and Chuanwei Lu and Hongjian Wu}, title={Semantic Analysis of Massive Text under Multi-Model Strategy}, proceedings={Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China}, publisher={EAI}, proceedings_a={ICIDC}, year={2022}, month={10}, keywords={dbscn; tf-idf; nlp; word2vec}, doi={10.4108/eai.17-6-2022.2322807} }- Zekun Tao
Youwei Zhang
Feiyue Fang
Jing Li
Chuanwei Lu
Hongjian Wu
Year: 2022
Semantic Analysis of Massive Text under Multi-Model Strategy
ICIDC
EAI
DOI: 10.4108/eai.17-6-2022.2322807
Abstract
The comment text generated by tourists' travel is one of the core contents of the research on semantic analysis of tourist destinations. Considering the phenomenon of fake reviews, simple copying, worthless information and irrelevant content, it prevents tourists from obtaining valuable information from online reviews. In this paper, the analysis of tourist reviews based on a multi-model fusion of natural language processing can solve the understanding problem with online reviews, and realize the analysis on characteristics of tourist destinations after machine processing. The method in this paper is experimentally verified on the data of question C in the ninth “Teddy Cup” Data Mining challenge, and the effective text is extracted for analysis of characteristics. It provides research ideas and methodological support for exploring the effectiveness and characteristic analysis of the text.
