Multilingual NLP and Text Mining for Cultural and Geopolitical Analytics
Developing multilingual NLP and computational text analytics for long-horizon cultural change, geopolitical discourse, and policy-relevant historical data.
Research at a Glance
| Topic | Data and Methods | Main Finding | Key Papers |
|---|---|---|---|
| Cultural association shifts in Chinese texts | Multi-decade Chinese printed corpora, semantic association analysis | Results do not support a simple “rising positive individualism” narrative for 1950 to 1999; some collectivist associations remain durable. | (Hamamura et al., 2021; Hamamura et al., 2022) |
| Embedding geometry for information/cultural space | Word-embedding geometry and semantic-space modeling | Embedding-space methods provide computational tools for quantifying cocoon-like semantic structures. | (Xu et al., 2020) |
| Geopolitics in language: Sino-Japanese relations | Chinese corpora, Japanese parliamentary records, literature review | Sentiment/discourse patterns co-evolve with geopolitical context, including war-memory and maritime-dispute framing. | (Hamamura et al., 2025) |
| Historical advertising under conflict | 10,094 ads in modern Chinese newspapers, comparative modeling | Military conflict is associated with lower globalized framing in ads; market familiarity moderates the effect. | (Sun et al., 2024) |
Methods Stack
| Category | Methods |
|---|---|
| Computation | Diachronic embeddings, topic modeling, temporal semantic analysis |
| Data Engineering | Historical corpus construction, multilingual normalization |
| Inference | Longitudinal discourse comparison, multi-source triangulation |
Program Direction
- Build robust multilingual NLP methods for socially grounded and policy-relevant analytics.
- Pair method papers with domain studies in culture, geopolitics, and communication.
- Supervise PhD projects on corpus methods, interpretability, and cross-domain validation.
