2026-03-08T17:43:36Z https://gunma-u.repo.nii.ac.jp/oai

oai:gunma-u.repo.nii.ac.jp:02000651 2025-03-12T07:00:48Z 325:326:1741758237716

A feature extraction approach for free-form text data based on discrete optimization and word embedding models: text data analysis of mobility survey at Gunma University 離散最適化と単語分散表現に基づく自由記述データの特徴抽出 ―群馬大学におけるモビリティ意識調査のデータ分析― NAGANO, Kiyohito 永野, 清仁 自然言語処理 離散最適化 データ分析 モビリティ Departmental Bulletin Paper Converting words, sentences, and text into vectors makes it easier to handle text data numerically. In natural language processing, converting words into vectors is called word embedding. This paper deals with a feature extraction approach that combines a word embedding model and a submodule optimization algorithm, and we analyze free-form text data in mobility surveys conducted at Gunma University from 2020 to 2022. departmental bulletin paper 群馬大学社会情報学部 2025-03-03 VoR application/pdf 群馬大学社会情報学部研究論集 32 69 88 AN10477040 1346-8812 https://gunma-u.repo.nii.ac.jp/record/2000651/files/32-P69-88.pdf http://hdl.handle.net/10087/0002000651 https://gunma-u.repo.nii.ac.jp/records/2000651 jpn open access