An Approach of Analyzing Movement Patterns Using Word Embeddings from Geo-tagged Tweets
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- Hirota Masaharu
- Department of Information Science, Okayama University of Science
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- Oda Tetsuya
- Department of Information and Computer Engineering, Okayama University of Science
説明
Many people share content about daily events on social media sites. Understanding people's movement patterns using the contents benefits numerous applications, such as tourism recommendation, city planning, and geo-targeting. This study proposes a new approach for clustering user trajectories to discover movement patterns. Our approach generates feature vectors for movement patterns using the word embedding model to learn movements between a pair of areas quantized by their latitude and longitude. Then, our approach uses multiple models to learn each area of different sizes and integrates the generated embedding vectors. The vectors represent the relationships between the movements from one area to the next. We demonstrated that clustering results by our proposed method.
収録刊行物
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- 人工生命とロボットに関する国際会議予稿集
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人工生命とロボットに関する国際会議予稿集 27 34-37, 2022-01-20
株式会社ALife Robotics
- Tweet
詳細情報 詳細情報について
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- CRID
- 1390854717504302080
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- ISSN
- 21887829
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- OpenAIRE
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- 抄録ライセンスフラグ
- 使用不可