A Textual Semantic Analysis Framework Integrating Geographic Metaphors and GIS-Based Spatial Analysis Methods
Abstract
1. Introduction
2. Related Work
2.1. Geographic Space and Semantic Space
2.2. Spatial Correlation and Semantic Correlation
2.3. Current Challenges and the Contributions of This Work
- (1)
- Semantic boundary delineation. Although semantic distances between primitives can be computed in high-dimensional semantic space, there remains a lack of systematic standards for defining the semantic scope of individual primitives. Consequently, existing studies primarily rely on subjective thresholds or manual heuristics to delineate semantic boundaries, leading to instability and limited generalizability. Moreover, mainstream semantic modeling approaches are developed based on models lacking explicit mechanisms for boundary delineation, such as bag-of-words models or topic models [6,13,50], which makes it difficult for them to distinguish core semantic clusters from transitional zones.
- (2)
- Semantic clustering pattern identification. Previous studies predominantly focus on measuring semantic correlation or representing co-occurrence networks, leaving the discovery of latent semantic patterns largely underexplored. Furthermore, current methods mainly adopt static clustering strategies, failing to capture the symmetric, hierarchical, and context-dependent nature of semantic associations [10,12,51]. The absence of well-defined semantic boundaries also hampers the identification of underlying semantic clustering patterns.
- (3)
- Knowledge evolution representation. Current methods for visualizing knowledge evolution mainly rely on time-series charts or 2D planar layouts, which are insufficient for clearly illustrating dynamic processes such as semantic expansion and migration. This limitation becomes more pronounced when handling cross-temporal, multimodal, and heterogeneous data, where 2D visualizations struggle to represent the complex multidimensional dynamics of semantic evolution. As a result, critical details regarding semantic relationship changes and structural reconfigurations are often omitted [52,53,54,55]. These limitations reduce the interpretability of visualizations and impair the analytical utility of such representations in understanding knowledge evolution.
3. Method
3.1. Semantic Space Modeling
3.1.1. Workflow
3.1.2. Dimensionality Reduction
3.2. Semantic Correlation Analysis
3.2.1. Global Semantic Correlations
3.2.2. Local Semantic Correlations
3.3. Knowledge Evolution Representation
4. Experiment
4.1. Data
4.2. Results
4.2.1. Semantic Boundary Identification
4.2.2. Semantic Clustering Pattern Identification
4.2.3. Analysis of Knowledge Evolution
5. Discussion
5.1. Overall
5.2. Significance
5.3. Limitations and Future Work
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. Chinese–English Correspondence Table for the 200 Most Frequent Semantic Primitives
ID | Chinese | English | ID | Chinese | English | ID | Chinese | English |
1 | 学霸 | Students with excellent grades | 68 | 男女 | Male and female | 135 | 阴暗面 | The dark side |
2 | 司法考试 | Judicial Examination | 69 | 名人 | Celebrity | 136 | 指甲 | Nails |
3 | 考试成绩 | Examination results | 70 | 印象 | Impression | 137 | 矮个子 | Short person |
4 | 难事 | Difficult things | 71 | 同学聚会 | Class reunion | 138 | 住院 | Hospitalization |
5 | 婚姻 | Marriage | 72 | 人生观 | Outlook on life | 139 | 偶遇 | Chance encounter |
6 | 社交 | Social interaction | 73 | 热情 | Enthusiasm | 140 | 学生 | Student |
7 | 班主任 | Head teacher | 74 | 异性 | Opposite sex | 141 | 优点 | Advantages |
8 | 书籍 | Books | 75 | 消极 | Negative | 142 | 朋友 | friend |
9 | 流眼泪 | Shed tears | 76 | 阻力 | Resistance | 143 | 健身房 | Gymnasium |
10 | 前任 | Ex | 77 | 外向 | Outgoing | 144 | 本科毕业 | Bachelor’s degree graduate |
11 | 小腿 | Calf | 78 | 研究生 | Graduate student | 145 | 作文 | Composition |
12 | 不聪明 | Not smart | 79 | 隔阂 | Estrangement | 146 | 编程 | Programming |
13 | 天赋 | Talent | 80 | 心声 | Voice of the Heart | 147 | 胜任 | Competent |
14 | 悲观主义 | Pessimism | 81 | 样貌 | Appearance | 148 | 寝室 | Dormitory |
15 | 同龄人 | Peers of the same age | 82 | 笔记 | Notes | 149 | 马甲线 | Six-pack abs |
16 | 考场 | Examination room | 83 | 肠胃 | Stomach and intestines | 150 | 羁绊 | Bond |
17 | 物理 | Physics | 84 | 勇气 | Courage | 151 | 论文 | Thesis |
18 | 成绩差 | Poor grades | 85 | 眼泪 | Tears | 152 | 老婆 | Wife |
19 | 剃光头 | Shave one’s head | 86 | 心痛 | Heartache | 153 | 全班 | The whole class |
20 | 微积分 | Calculus | 87 | 习惯 | Habit | 154 | 泪水 | Tears |
21 | 缺点 | Disadvantages | 88 | 好身材 | Good figure | 155 | 男友 | Boyfriend |
22 | 境界 | Realm | 89 | 理想 | Ideal | 156 | 容貌 | Appearance |
23 | 思维 | Thinking | 90 | 文化 | Culture | 157 | 脸红 | Blush |
24 | 做作业 | Do homework | 91 | 金钱 | Money | 158 | 三围 | Three dimensions |
25 | 疤痕 | Scar | 92 | 女孩 | Girl | 159 | 距离 | Distance |
26 | 视力 | Vision | 93 | 消磨时光 | Kill time | 160 | 肌肤 | Skin |
27 | 孝义 | Filial piety and righteousness | 94 | 仇恨 | Hate | 161 | 能量 | Energy |
28 | 计算机 | Computer | 95 | 屁股 | Buttocks | 162 | 男生 | Male |
29 | 皮肤 | Skin | 96 | 短腿 | Short legs | 163 | 弱点 | Weakness |
30 | 事业 | Career | 97 | 情侣 | Couple | 164 | 单身 | Single |
31 | 关心 | Care | 98 | 内向 | Introverted | 165 | 女生 | Girl |
32 | 医生 | Doctor | 99 | 父母 | Parents | 166 | 能力 | Ability |
33 | 心理 | Psychology | 100 | 爱情 | Love | 167 | 内心 | Inner self |
34 | 心态 | Mindset | 101 | 主观 | Subjective | 168 | 话语权 | The right to speak |
35 | 恋人 | Lover | 102 | 家事 | Family affairs | 169 | 躯壳 | Body shell |
36 | 权势 | Power and influence | 103 | 态度 | Attitude | 170 | 不公 | Injustice |
37 | 兄弟姐妹 | Brothers and sisters | 104 | 心动 | Heartbeat | 171 | 价值 | Value |
38 | 胸部 | Chest | 105 | 妈妈 | Mom | 172 | 人缘 | Popularity |
39 | 嘴唇 | Lips | 106 | 真情 | True feelings | 173 | 青春痘 | Acne |
40 | 心情 | mood | 107 | 同学 | classmate | 174 | 三观 | Three Views |
41 | 单眼皮 | Single eyelid | 108 | 赘肉 | Excess flesh | 175 | 地位 | Status |
42 | 人性 | Human nature | 109 | 腹肌 | Abdominal muscles | 176 | 爸妈 | Parents |
43 | 心脏 | Heart | 110 | 大舌头 | Big tongue | 177 | 心虚 | Feeling guilty |
44 | 幻想 | Fantasy | 111 | 生疏 | Unfamiliarity | 178 | 读书 | Study |
45 | 流泪 | Shed tears | 112 | 亲人 | Relatives | 179 | 天生 | Born |
46 | 下唇 | Lower lip | 113 | 家境 | Family background | 180 | 风度 | Grace |
47 | 德语 | German | 114 | 体态 | Posture | 181 | 外界 | Outside |
48 | 好孩子 | good kid | 115 | 责任 | Responsibility | 182 | 背单词 | Memorize words |
49 | 作品 | Work | 116 | 谎言 | Lie | 183 | 竞争力 | Competitiveness |
50 | 本性 | Nature | 117 | 财富 | Wealth | 184 | 尖子生 | Top student |
51 | 主见 | Opinion | 118 | 女友 | Girlfriend | 185 | 悲剧 | Tragedy |
52 | 兴趣 | Interest | 119 | 本能 | Instinct | 186 | 男性 | Male |
53 | 信心 | Confidence | 120 | 胖子 | Fat man | 187 | 光头 | Bald head |
54 | 书包 | Schoolbag | 121 | 感情 | Emotion | 188 | 亲戚家 | Relatives’ home |
55 | 文凭 | Diploma | 122 | 胖子 | Fat man | 189 | 美貌 | Beauty |
56 | 雀斑 | Freckles | 123 | 感情 | Emotion | 190 | 环境 | Environment |
57 | 交通 | Transportation | 124 | 事情 | The matter | 191 | 知识分子 | Intellectual |
58 | 世事 | Worldly affairs | 125 | 前额 | Forehead | 192 | 对象 | Boyfriend/Girlfriend |
59 | 同情 | Sympathy | 126 | 钟情 | Infatuation | 193 | 命运 | Fate |
60 | 手臂 | Arm | 127 | 单亲 | Single parent | 194 | 英语 | English |
61 | 颜控 | Beauty enthusiast | 128 | 不漂亮 | Not beautiful | 195 | 中意 | Fancy |
62 | 颜值 | Appearance level | 129 | 套路 | Trick | 196 | 秃顶 | Baldness |
63 | 状态 | Status | 130 | 平胸 | Flat chest | 197 | 爱意 | Love |
64 | 全职太太 | Full-time housewife | 131 | 哥们 | Buddy | 198 | 负面 | Negative |
65 | 人脉 | Connections | 132 | 爷爷奶奶 | Grandpa and Grandma | 199 | 亲和力 | Affinity |
66 | 疼爱 | Love and care | 133 | 上学 | Go to school | 200 | 脾气 | Temper |
67 | 婆婆 | Mother-in-law | 134 | 女朋友 | Girlfriend |
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ID | Semantic Primitive | X | Y | English |
---|---|---|---|---|
1 | 傲气 | −0.859604166 | 1.984212902 | Arrogant |
2 | 倔强 | 0.643495166 | −1.313592229 | Obstinate |
3 | 天生 | −0.573996029 | −1.886607475 | Innate |
4 | 偏执 | 1.278016735 | −0.601750412 | Stubborn |
5 | 强势 | 0.515507556 | 1.197673322 | Mighty |
6 | 功利 | −1.143747358 | 0.450880872 | Benthamism |
7 | 骗子 | −1.37262951 | 0.895490501 | Fraud |
8 | 无理 | −0.612900011 | −1.342502749 | Unreasonable |
9 | 骄傲 | −0.122557241 | 0.501880319 | Pride |
10 | 自私 | 0.854270125 | −0.839828016 | Selfishness |
Value | Z-Score | p-Value | Variance | Expectation | |
---|---|---|---|---|---|
Getis-Ord G | 0.9994 | 0.3673 | 0.0478 | 0.0001 | 0.1992 |
Moran’s I | 0.0304 | 2.3418 | 0.0120 | 0.0009 | 0.0027 |
Geary’s | 0.5876 | −1.2565 | 0.0410 | 0.0002 | 1.0000 |
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Liu, Y.; Ren, Z.; Wang, K.; Tian, Q.; Kuai, X.; Li, S. A Textual Semantic Analysis Framework Integrating Geographic Metaphors and GIS-Based Spatial Analysis Methods. Symmetry 2025, 17, 1064. https://doi.org/10.3390/sym17071064
Liu Y, Ren Z, Wang K, Tian Q, Kuai X, Li S. A Textual Semantic Analysis Framework Integrating Geographic Metaphors and GIS-Based Spatial Analysis Methods. Symmetry. 2025; 17(7):1064. https://doi.org/10.3390/sym17071064
Chicago/Turabian StyleLiu, Yu, Zhen Ren, Kaifeng Wang, Qin Tian, Xi Kuai, and Sheng Li. 2025. "A Textual Semantic Analysis Framework Integrating Geographic Metaphors and GIS-Based Spatial Analysis Methods" Symmetry 17, no. 7: 1064. https://doi.org/10.3390/sym17071064
APA StyleLiu, Y., Ren, Z., Wang, K., Tian, Q., Kuai, X., & Li, S. (2025). A Textual Semantic Analysis Framework Integrating Geographic Metaphors and GIS-Based Spatial Analysis Methods. Symmetry, 17(7), 1064. https://doi.org/10.3390/sym17071064