| 摘要: |
| 城市公园作为公共绿色空间在提升居民生
活质量方面发挥重要作用，但公众对不同类型城市公
园的感知和需求差异在公园规划与管理中尚未得到充
分体现。本研究旨在构建自下而上的公众感知分析框
架，揭示大运河沿线不同类型公园的独特感知维度及
其情感偏向性，为城市公园的分类优化和功能结构调
整提供科学依据。本研究以苏州市大运河沿线公园为
研究对象，基于公园评论数据，采用集成Jieba分词、
TF-IDF、LDA主题模型和情感倾向分析方法，结合
IPA分析法，深入分析公众对不同类型公园的感知和
情感倾向。研究发现，综合公园和专类公园因其深厚
的文化底蕴和丰富的景观资源，深受公众青睐，且在
智能化设施和无障碍设施的需求上尤为突出；社区公
园和游园的关注焦点则集中在服务效能和空间布局的
合理性，尤其是在日常生活便利性和社区活动支持性
方面。通过分析公众感知数据，研究提炼出了一系列
关键感知指标，为公园规划和改进提供了具体决策依
据。研究丰富了运河沿线城市公园评价方法，展示了
网络文本分析在揭示公众需求与优化城市公园功能结
构方面的潜力，对大运河沿线城市公园规划与管理实
践提供了借鉴意义。 |
| 关键词: 城市公园 文本分析 大运河 机器学
习 优化途径 |
| DOI：10.13791/j.cnki.hsfwest.20231220007 |
| 分类号: |
| 基金项目:教育部人文社会科学研究规划基金项目（23YJAZH094）；江苏高校优势学科建设工程四期项目；江苏省研究生科研创新项目（KYCX21_3052） |
|
| Research on perception and classification optimization of urban parks along the GrandCanal based on network text analysis: Taking Suzhou Section as an example |
|
WANG Shuai,LYU Fei
|
| Abstract: |
| Urban parks, as key components of green infrastructure, serve dual functions:
ecological services and recreational spaces for residents. With the rapid pace of urbanization
and the diversification of public needs, optimizing park planning and management has become
an urgent issue. Parks with cultural attributes, such as those along the Grand Canal, not only
serve as urban landscapes but also carry significant cultural heritage. The integration of cultural
preservation and leisure functions in park planning is essential for maintaining both ecological
balance and the cultural identity of a city. Existing research on public perceptions largely
focuses on the cultural heritage along the Grand Canal or historical cultural districts, with
limited attention given to the role of public perception in the planning of cultural parks along
the canal. This paper aims to address this gap by exploring public perceptions of urban parks
along the Grand Canal, utilizing network text analysis and machine learning to analyze largescale
public commentary data. This study specifically examines parks along the Grand Canal in
Suzhou, which is part of the Grand Canal’s cultural heritage and recognized as a UNESCO
World Heritage site. These parks are not only recreational areas but also carry deep historical
and cultural significance. The research focuses on categorizing and optimizing public
perceptions through the analysis of public comments, integrating sentiment analysis, topic
modeling, and classification techniques. By capturing the emotional tendencies and
experiences of park users, this research aims to inform urban park planning that aligns with
public needs and cultural expectations, thereby enhancing the effectiveness of cultural resource
utilization and promoting heritage preservation. The data for this study was collected from
social media platforms such as Ctrip and Dianping, which provide user-generated content and
feedback on parks along the Grand Canal. A total of 29 608 public comments were gathered
between 2020 and 2023, focusing on 39 parks. After data cleaning and processing, 15 043 valid
comments were retained for analysis. The parks included in the study are categorized into four
types: comprehensive parks, specialized parks, community parks, and urban gardens. Using
Python-based text mining tools, the comments were processed to extract significant keywords
and sentiments. A combination of natural language processing (NLP) techniques, including
Jieba word segmentation and TF-IDF (Term Frequency-Inverse Document Frequency)
analysis, was employed to extract key perceptual words from the comments. These words were
then analyzed using Latent Dirichlet Allocation (LDA) topic modeling to identify prevalentthemes within the comments. The LDA model revealed three primary categories of public perception: 1) public experience and facility management, 2) park
design and functional layout, and 3) cultural heritage and community participation. Each category was further subdivided into more specific themes, such as
smart facilities, landscape design, and cultural connotations. Sentiment analysis was performed to classify each comment as positive, neutral, or negative based
on the presence of sentiment-bearing words. This was followed by an Importance-Performance Analysis (IPA) to identify areas in need of improvement. The
analysis revealed several key insights into public perceptions of different park types. Comprehensive and specialized parks were found to face common issues
regarding the provision of smart facilities, accessibility for disabled individuals, landscape design, and cultural representation. Community parks, on the other
hand, were primarily concerned with service facilities, spatial vitality, and night lighting, while urban gardens faced challenges related to parking, functionality,
and a sense of community attachment. The study also employed clustering techniques to identify overlapping issues in parks along the Grand Canal. It was
found that comprehensive and specialized parks, due to their larger scale and tourist-focused services, shared concerns related to the integration of smart
technologies, accessibility for the disabled, and the adequate display of cultural heritage. Community parks and urban gardens, being smaller in size and closer
to residential areas, had distinct issues regarding service facility provision, spatial planning, and the ability to foster a sense of community. Based on these
findings, the study provides several recommendations for optimizing urban park planning along the Grand Canal. For comprehensive and specialized parks,
improvements in smart technologies, barrier-free facilities, and better integration of cultural heritage in park design are suggested. For community parks, there
is a need for enhanced service facilities, such as better lighting, parking, and functional layouts to support community activities. Lastly, urban gardens could
benefit from improving their sense of place and offering more practical spaces for local residents. This research underscores the importance of public perception
in the planning and development of urban parks. By integrating public feedback through modern data analysis methods, such as network text analysis, this
study provides valuable insights for urban planners and policymakers aiming to optimize park spaces for both cultural heritage preservation and community
engagement. |
| Key words: city parks text analytics the Grand Canal machine learning optimization pathways |
