Application of Semantic Analysis Technology in Natural Language Processing

Beibei Zhang; Hao Yan; Jiawei Wu; Ping Qu

doi:10.5281/zenodo.12601286

Authors

Beibei Zhang Xi'an Jiaotong University
Hao Yan Syracuse University
Jiawei Wu Illinois Institute of Technology
Ping Qu Maharishi International University

DOI:

https://doi.org/10.5281/zenodo.12601286

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n2a04

PURL:

https://purl.archive.org/suas/JCTAM.v1n2a04

References:

51

Keywords:

Artificial intelligence Semantic Analysis, User Research, Natural Language Processing, User Experience Design

Abstract

With the evolution of intelligent technology, especially in the field of natural language processing, semantic analysis has become a powerful tool for studying users. It is good at processing and interpreting numerous unformatted information texts produced in user interactions. This article takes an in-depth look at how to improve the user experience design process using intelligent semantic analysis technology, which can quickly and accurately extract important information from user feedback through automated analysis. Although some current semantic analysis technologies face accuracy tests when analyzing data rich in contextual information, by integrating algorithms such as TF-IDF and Word2vec with deep learning models, the accuracy and efficiency of analysis and interpretation can be achieved significantly improved. In particular, this study developed a semantic clustering analysis technology for short texts, confirming its significant effect in classifying user feedback and assisting product design decision-making. In the future, with technological advancement, artificial intelligence is expected to be routinely used in consumer research, thereby making product design closer to user needs.

Author Biographies

Beibei Zhang, Xi'an Jiaotong University

Software Engineering, Xi'an Jiaotong University, Xi'an, China.

Hao Yan, Syracuse University

Engineering and Computer Science, Syracuse University, Syracuse, NY, USA.

Jiawei Wu, Illinois Institute of Technology

Engineering in Artificial Intelligence for Computer Vision and Control, Illinois Institute of Technology, Chicago, IL, USA.

Ping Qu, Maharishi International University

Computer Science, Maharishi International University, Fairfield, IA, USA.

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