How to quantify multidimensional perception of urban parks? Integrating deep learning-based social media data analysis with questionnaire survey methods

IF 6 2区 环境科学与生态学 Q1 ENVIRONMENTAL STUDIES
Wenwen Huang , Xukai Zhao , Guangsi Lin , Zhifang Wang , Mengyun Chen
{"title":"How to quantify multidimensional perception of urban parks? Integrating deep learning-based social media data analysis with questionnaire survey methods","authors":"Wenwen Huang ,&nbsp;Xukai Zhao ,&nbsp;Guangsi Lin ,&nbsp;Zhifang Wang ,&nbsp;Mengyun Chen","doi":"10.1016/j.ufug.2025.128754","DOIUrl":null,"url":null,"abstract":"<div><div>Urban parks are places where people regularly connect with nature and each other. Quantifying perceptions of urban parks presents significant changes. Recently, social media data has been increasingly used for studying landscape perceptions, preferences, and management. With the advent of deep learning techniques, the performance of NLP tasks has seen considerable improvement. We posed research questions at the methodological level: How could deep learning-based NLP methods be constructed to assess the multidimensional perception (MDP) of urban parks? How could the assessment performance of this method be validated? In this study, we constructed an MDP of urban parks assessment model based on ERNIE and subsequently conducted a questionnaire survey. By comparing the differences and similarities between the two data sets, we verified the model's assessment performance and proposed the application potential of deep learning-based methods. The findings indicated: (1) our model effectively obtained and assessed sentiment information from online reviews about park accessibility, safety, aesthetics, attractiveness, maintenance, and usability with an accuracy rate exceeding 80 %. (2) The questionnaire survey data confirmed the model's high efficacy, showing consistency in accessibility, aesthetics, and maintenance, but inconsistency in attractiveness and usability due to differences in data expression and timeliness. (3) Deep learning-based NLP methods significantly enhanced sentiment analysis of social media data, showing great potential for practical applications. The results could enhance the performance of sentiment analysis on social media data, serving as a decision-aid tool for park managers and policymakers, and providing valuable insights and guidance for park construction and management.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"107 ","pages":"Article 128754"},"PeriodicalIF":6.0000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Urban Forestry & Urban Greening","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1618866725000883","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0

Abstract

Urban parks are places where people regularly connect with nature and each other. Quantifying perceptions of urban parks presents significant changes. Recently, social media data has been increasingly used for studying landscape perceptions, preferences, and management. With the advent of deep learning techniques, the performance of NLP tasks has seen considerable improvement. We posed research questions at the methodological level: How could deep learning-based NLP methods be constructed to assess the multidimensional perception (MDP) of urban parks? How could the assessment performance of this method be validated? In this study, we constructed an MDP of urban parks assessment model based on ERNIE and subsequently conducted a questionnaire survey. By comparing the differences and similarities between the two data sets, we verified the model's assessment performance and proposed the application potential of deep learning-based methods. The findings indicated: (1) our model effectively obtained and assessed sentiment information from online reviews about park accessibility, safety, aesthetics, attractiveness, maintenance, and usability with an accuracy rate exceeding 80 %. (2) The questionnaire survey data confirmed the model's high efficacy, showing consistency in accessibility, aesthetics, and maintenance, but inconsistency in attractiveness and usability due to differences in data expression and timeliness. (3) Deep learning-based NLP methods significantly enhanced sentiment analysis of social media data, showing great potential for practical applications. The results could enhance the performance of sentiment analysis on social media data, serving as a decision-aid tool for park managers and policymakers, and providing valuable insights and guidance for park construction and management.
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
11.70
自引率
12.50%
发文量
289
审稿时长
70 days
期刊介绍: Urban Forestry and Urban Greening is a refereed, international journal aimed at presenting high-quality research with urban and peri-urban woody and non-woody vegetation and its use, planning, design, establishment and management as its main topics. Urban Forestry and Urban Greening concentrates on all tree-dominated (as joint together in the urban forest) as well as other green resources in and around urban areas, such as woodlands, public and private urban parks and gardens, urban nature areas, street tree and square plantations, botanical gardens and cemeteries. The journal welcomes basic and applied research papers, as well as review papers and short communications. Contributions should focus on one or more of the following aspects: -Form and functions of urban forests and other vegetation, including aspects of urban ecology. -Policy-making, planning and design related to urban forests and other vegetation. -Selection and establishment of tree resources and other vegetation for urban environments. -Management of urban forests and other vegetation. Original contributions of a high academic standard are invited from a wide range of disciplines and fields, including forestry, biology, horticulture, arboriculture, landscape ecology, pathology, soil science, hydrology, landscape architecture, landscape planning, urban planning and design, economics, sociology, environmental psychology, public health, and education.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信