Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments
Abstract
:1. Introduction
- To develop a novel and high-accuracy machine-learning-based method for analyzing youth sentiment in Shanghai.
- To identify the determinants of sentiment as well as its relationship with the built environment in Shanghai.
- Through the machine learning technology, which can be utilized as a sentiment feedback system for improvement of urban construction.
2. Literature Reviews
2.1. Research on the Relationship between Sentiment and Spatial Environment
2.2. Text Sentiment Analysis
2.2.1. The Sentiment-Dictionary-Based Method
2.2.2. Machine-Learning-Based Method
2.3. Research on the Spatial Environment
3. Materials and Methods
3.1. Study Area and Data Sources
3.2. Text Sentiment Classification
3.2.1. Dataset for Training and Testing
3.2.2. Framework
3.2.3. Pretrained Vocabulary Vector
3.2.4. Baseline Module
3.2.5. Refinement Module
- Multi-Loss Constraint
- 2.
- Data Augmentation
3.2.6. Reanalysis Module
3.3. Built Environment
3.3.1. Land Use Degree
3.3.2. Job–Housing Relationship
3.3.3. Road Traffic
3.3.4. Green Rate
3.3.5. Service Facilities
4. Results
4.1. Text Sentiment Analysis
4.2. Built Environment Analysis
4.3. Correlation Analysis
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Researchers | Built Environment Evaluation Elements | ||||
---|---|---|---|---|---|
Social Env. | Land Use | Road Traffic | Eco-Space | Public Service | |
Lin et al. [54] | Population density, Deviation index of employment and residence | Buses available | Green rate | Diversity of public facilities | |
Lv et al. [55] | Population density | Urban spatial structure | Bus station density, Street height to width ratio | Green view rate Sky openness | |
Xie et al. [56] | Job–housing relationship | Land use intensity | Residents’ travel behavior | ||
Xu et al. [57] | Soundscape | ||||
Long et al. [58] | Street crossing facilities, Motor vehicle and non-motor vehicle isolation, Walkway width | Street greening | Street facilities | ||
Leslie [59] | Land use | Traffic safety, Street connectivity, Traffic flow | Green rate | Infrastructure | |
Ettema [60] | Attractiveness | Accessibility, Traffic safety | Facilities | ||
Ewing [61] | Land use | Accessibility | Green rate | ||
Yuan [62] | Population density | Land use degree, Land type, Residential land ratio | Bus station density, Density of road network, Intersection density | Green rate | |
Wang et al. [63] | Land use degree | Density of road network, Buses available | Density of public service |
ID | Time | Longitude | Latitude | Weibo Comments |
---|---|---|---|---|
1 | 19 July 13:43:43 | 121.4861 | 31.23672 | Chinese Comments: ‘好久不更博,最近把微博给忘了’. English Comments: ‘I forgot about Weibo recently, it’s been a while since I updated’. |
2 | 19 July 17:21:14 | 121.4422 | 31.22382 | Chinese Comments: ‘失踪人口回归’. English Comments: ‘Return of missing persons’. |
3 | 19 July 00:18:53 | 121.4446 | 31.22577 | Chinese Comments: ‘吃六个带两个回家,上海限定豫园奶昔也太好喝’. English Comments: ‘Eat six and take two home, Shanghai limited Yuyuan milkshakes are too good’. |
Method | Score (Acc %) |
---|---|
FastText | 0.639 |
TextCNN | 0.657 |
TextRCNN | 0.645 |
Transformer | 0.650 |
Ours | 0.697 |
District | Sentiment Label | ||||||
---|---|---|---|---|---|---|---|
Happy | Surprise | Neutral | Angry | Sad | Fear | Total | |
Huangpu | 380 | 32 | 649 | 182 | 301 | 0 | 1544 |
Yangpu | 84 | 12 | 145 | 62 | 83 | 0 | 386 |
Hongkou | 77 | 8 | 137 | 41 | 70 | 0 | 333 |
Jingan | 222 | 13 | 292 | 107 | 155 | 0 | 789 |
Putuo | 107 | 3 | 136 | 67 | 83 | 1 | 397 |
Changning | 131 | 9 | 166 | 58 | 113 | 0 | 477 |
Xuhui | 245 | 14 | 314 | 134 | 167 | 2 | 876 |
Chongming | 22 | 1 | 25 | 14 | 10 | 0 | 72 |
Fengxian | 54 | 4 | 67 | 27 | 31 | 0 | 183 |
Qingpu | 154 | 8 | 252 | 64 | 85 | 0 | 563 |
Songjiang | 154 | 6 | 200 | 94 | 131 | 1 | 586 |
Jinshan | 29 | 1 | 34 | 23 | 33 | 0 | 120 |
Pudong | 684 | 48 | 900 | 370 | 516 | 4 | 2522 |
Jiading | 131 | 8 | 131 | 74 | 111 | 1 | 456 |
Baoshan | 122 | 10 | 133 | 80 | 108 | 0 | 453 |
Minhang | 310 | 22 | 419 | 152 | 256 | 0 | 1159 |
Total | 2906 | 199 | 4000 | 1549 | 2253 | 9 | 10,916 |
District | Happy | Surprise | Angry | Sad | ||||
---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | |
Huangpu | 0.78 | 0.45 | −0.09 | 0.90 | 1.19 | 0.97 | −0.58 | 0.90 |
Yangpu | 0.71 | 0.44 | −0.17 | 0.31 | 1.06 | 0.90 | −0.64 | 0.74 |
Hongkou | 0.78 | 0.51 | −0.38 | 0.23 | 1.27 | 0.73 | −0.61 | 1.09 |
Jingan | 0.70 | 0.52 | −0.35 | 0.40 | 1.24 | 0.80 | −0.54 | 0.93 |
Putuo | 0.71 | 0.39 | 1.00 | 0.67 | 1.06 | 0.83 | −0.45 | 0.92 |
Changning | 0.71 | 0.43 | −0.11 | 0.77 | 1.10 | 0.85 | −0.55 | 0.96 |
Xuhui | 0.74 | 0.43 | −0.07 | 0.78 | 1.15 | 0.87 | −0.52 | 1.02 |
Minhang | 0.66 | 0.53 | 0.09 | 0.72 | 1.33 | 0.88 | −0.67 | 0.89 |
Fengxian | 0.80 | 0.64 | −0.50 | 0.25 | 1.19 | 0.97 | −0.77 | 0.50 |
Qingpu | 0.75 | 0.43 | −0.13 | 0.61 | 1.09 | 0.99 | −0.42 | 1.16 |
Songjiang | 0.69 | 0.54 | 1.17 | 0.47 | 1.35 | 0.55 | −0.73 | 0.86 |
Jinshan | 0.72 | 0.48 | 1.00 | 0.00 | 1.26 | 0.63 | −0.33 | 1.19 |
Pudong | 0.69 | 0.54 | −0.31 | 0.67 | 1.19 | 0.81 | −0.65 | 0.87 |
Jiading | 0.77 | 0.50 | −0.50 | 0.50 | 1.05 | 0.92 | −0.46 | 1.08 |
Baoshan | 0.81 | 0.46 | −0.10 | 0.09 | 1.14 | 0.82 | −0.65 | 0.86 |
Chongming | 0.73 | 0.38 | 0.00 | 0.00 | 0.93 | 1.07 | −0.30 | 1.01 |
District | Land Use Degree | Job–Housing Relationship | Road Network (km/km2) | Transportation Station | Green Rate | Shopping Facilities | Food Services | Entertainment Facilities | Medical Services | Exercise Facilities |
---|---|---|---|---|---|---|---|---|---|---|
Huangpu | 0.52 | 1.32 | 17.86 | 1.32 | 0.14 | 694.03 | 230.7 | 29.7 | 44.85 | 25.85 |
Yangpu | 0.65 | 0.56 | 13.77 | 0.45 | 0.11 | 207.49 | 99.56 | 14.02 | 23.29 | 11.69 |
Hongkou | 0.53 | 0.67 | 17.05 | 0.77 | 0.1 | 362.53 | 159 | 20.34 | 39.36 | 20.08 |
Jingan | 0.67 | 1.01 | 16.55 | 0.71 | 0.12 | 508.99 | 183.57 | 21.09 | 40.19 | 21.28 |
Putuo | 0.71 | 0.61 | 15.76 | 0.4 | 0.24 | 243.69 | 96.37 | 13.12 | 20.85 | 12.31 |
Changning | 0.68 | 0.90 | 16.81 | 0.48 | 0.31 | 248.88 | 122.44 | 16.64 | 32.07 | 17.26 |
Xuhui | 0.63 | 1.07 | 16.27 | 0.63 | 0.22 | 232.15 | 114.84 | 14.79 | 24.8 | 17.09 |
Minhang | 0.74 | 0.51 | 6.73 | 0.27 | 0.21 | 66.06 | 31.01 | 4.36 | 4.65 | 3.53 |
Fengxian | 0.76 | 0.37 | 3.94 | 0.15 | 0.47 | 21.1 | 6.25 | 1.11 | 1.38 | 0.47 |
Qingpu | 0.74 | 0.40 | 3.88 | 0.15 | 0.41 | 19.15 | 6.77 | 0.76 | 1.15 | 0.54 |
Songjiang | 0.74 | 0.35 | 5.15 | 0.17 | 0.42 | 30.36 | 13.94 | 2.05 | 2.03 | 1.22 |
Jinshan | 0.64 | 0.43 | 4.04 | 0.21 | 0.54 | 15.28 | 4.74 | 0.86 | 1.1 | 0.35 |
Pudong | 0.84 | 0.53 | 6.08 | 0.18 | 0.31 | 44.35 | 18.49 | 2.37 | 3.46 | 2.05 |
Jiading | 0.77 | 0.41 | 5.8 | 0.13 | 0.33 | 41.49 | 16.15 | 1.95 | 2.74 | 1.32 |
Baoshan | 0.78 | 0.30 | 6.67 | 0.13 | 0.55 | 72.46 | 27.9 | 3.8 | 4.69 | 2.64 |
Chongming | 0.77 | 0.41 | 2.9 | 0.13 | 0.62 | 4.04 | 0.78 | 0.49 | 0.34 | 0.1 |
Built Environment | Happy | Surprise | Angry | Sad |
---|---|---|---|---|
Land Use Degree | 0.207 | −0.143 | 0.132 | −0.238 |
Job–housing | 0.261 | −0.590 * | −0.106 | −0.135 |
Road Network | −0.292 | 0.462 | −0.036 | 0.016 |
Transportation | −0.168 | 0.436 | 0.061 | 0.042 |
Green Rate | −0.013 | −0.559 * | 0.03 | 0.051 |
Shopping Facilities | −0.162 | 0.402 | 0.055 | −0.003 |
Food Services | −0.198 | 0.413 | 0.024 | 0.013 |
Entertainment | −0.225 | 0.424 | 0.033 | −0.003 |
Medical Services | −0.205 | 0.37 | −0.003 | 0.034 |
Exercise Facilities | −0.234 | 0.436 | 0.016 | 0.04 |
Built Environment | Happy | Surprise | Angry | Sad |
---|---|---|---|---|
Land Use Degree | 0.014 * | 0.076 ** | −0.034 ** | −0.052 ** |
Job–housing | −0.003 | −0.012 | 0.007 | 0.021 ** |
Road Network | −0.044 ** | −0.212 ** | 0.102 ** | 0.166 ** |
Transportation | −0.003 | −0.103 ** | 0.034 ** | 0.074 ** |
Green Rate | 0.018 ** | 0.161 ** | −0.078 ** | −0.131 ** |
Shopping Facilities | −0.052 ** | −0.217 ** | 0.090 ** | 0.150 ** |
Food Services | −0.080 ** | −0.282 ** | 0.107 ** | 0.221 ** |
Entertainment | −0.064 ** | −0.242 ** | 0.106 ** | 0.194 ** |
Medical Services | −0.071 ** | −0.197 ** | 0.085 ** | 0.150 ** |
Exercise Facilities | −0.053 ** | −0.268 ** | 0.117 ** | 0.205 ** |
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Duan, S.; Shen, Z.; Luo, X. Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments. Int. J. Environ. Res. Public Health 2022, 19, 4794. https://doi.org/10.3390/ijerph19084794
Duan S, Shen Z, Luo X. Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments. International Journal of Environmental Research and Public Health. 2022; 19(8):4794. https://doi.org/10.3390/ijerph19084794
Chicago/Turabian StyleDuan, Sutian, Zhiyong Shen, and Xiao Luo. 2022. "Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments" International Journal of Environmental Research and Public Health 19, no. 8: 4794. https://doi.org/10.3390/ijerph19084794