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        <identifier>oai:glim-re.repo.nii.ac.jp:02003114</identifier>
        <datestamp>2025-07-16T05:51:52Z</datestamp>
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          <dc:title xml:lang="ja">心理学研究におけるテキストマイニングの活用</dc:title>
          <dc:title xml:lang="ja-Kana">シンリガク　ケンキュウ　ニオケル　テキスト　マイニング　ノ　カツヨウ</dc:title>
          <dc:title xml:lang="en">Application of Text Mining in Psychological Research</dc:title>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="ja">藏本, 知子</jpcoar:creatorName>
            <jpcoar:creatorName xml:lang="ja-Kana">クラモト, トモコ</jpcoar:creatorName>
            <jpcoar:creatorName xml:lang="en">Kuramoto, Tomoko</jpcoar:creatorName>
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          <jpcoar:subject subjectScheme="Other">自然言語処理</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">テキストデータ</jpcoar:subject>
          <jpcoar:subject subjectScheme="Other">対応分析</jpcoar:subject>
          <jpcoar:subject xml:lang="en" subjectScheme="Other">KH Coder</jpcoar:subject>
          <jpcoar:subject xml:lang="en" subjectScheme="Other">WordMiner</jpcoar:subject>
          <jpcoar:subject xml:lang="en" subjectScheme="Other">Natural language processing</jpcoar:subject>
          <jpcoar:subject xml:lang="en" subjectScheme="Other">Text data</jpcoar:subject>
          <jpcoar:subject xml:lang="en" subjectScheme="Other">Correspondence analysis</jpcoar:subject>
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          <datacite:description descriptionType="Abstract">テキストデータの分析手法として，近年テキストマイニング（text mining）が注目を集めている。本稿では，心理学研究におけるテキストマイニングの利点と留意点を論じる。テキストマイニングを活用することで，ＫＪ法など従来の手法では十分に確保できなかった結果の再現性を担保し，データ探索を容易にし，新たな発見を促すことが可能となる。しかし，心理学研究においてこのような利点を最大限に引き出すためには，いくつかの留意点が存在する。本稿では特にデータサイズの問題に注目し，その対処法として，明瞭な目的と収束的な質問，前処理の重要性について詳述する。さらに，プログラミングの知識が十分でなくても利用可能なテキストマイニングツールを紹介し，それらを連携させることによる高度な活用法を提案する。</datacite:description>
          <datacite:description descriptionType="Abstract">Text mining has recently attracted interest as a method for analyzing text data. This study discusses the advantages and considerations of text mining in psychological research. Through text mining, the reproducibility of results that cannot be sufficiently ensured by conventional methods (e.g. KJ method) can be ensured. Furthermore, it facilitates data exploration and encourages discoveries. However, considerations must be made to leverage these advantages in psychological research fully. This study focused on the issue of data size and elaborates on a clear purpose, convergent questions and the importance of pre-treatment in addressing the issue of data size. Additionally, text mining tools that can be used without requiring extensive programming knowledge are introduced. Advanced methods are proposed by linking these tools.</datacite:description>
          <datacite:description descriptionType="Other">研究論文</datacite:description>
          <dc:publisher xml:lang="ja">学習院大学人文科学研究所</dc:publisher>
          <datacite:date dateType="Issued">2025-03-10</datacite:date>
          <dc:language>jpn</dc:language>
          <dc:type rdf:resource="http://purl.org/coar/resource_type/c_6501">departmental bulletin paper</dc:type>
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          <jpcoar:identifier identifierType="HDL">http://hdl.handle.net/10959/0002003114</jpcoar:identifier>
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          <jpcoar:sourceIdentifier identifierType="PISSN">18817920</jpcoar:sourceIdentifier>
          <jpcoar:sourceTitle xml:lang="ja">人文</jpcoar:sourceTitle>
          <jpcoar:sourceTitle xml:lang="en">Jinbun</jpcoar:sourceTitle>
          <jpcoar:issue>23</jpcoar:issue>
          <jpcoar:pageStart>93</jpcoar:pageStart>
          <jpcoar:pageEnd>112</jpcoar:pageEnd>
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            <datacite:date dateType="Available">2025-06-04</datacite:date>
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