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• W4285024279 abstract "본 연구에서는 잡플래닛에서 익명으로 작성된 조직 평가 텍스트로부터 토픽모델링을 통해 주요 토픽을 추출하고 이직과 연관성을 탐색하고자 하였다. 토픽 추출을 위해 시가총액 기준 상위 50위 조직의 평가 텍스트 35,031개 문서를 대상으로 잠재의미분석(Latent Semantic Analysis; LSA)을 실시하였다. LSA 결과 추출된 차원은 Varimax 회전을 통해 해석을 시도하였고, 최종적으로 62개의 해석 가능한 토픽을 도출하였다. 개별 토픽은 내용의 관련성을 연구자가 판단하여 8가지 상위토픽군으로 묶어 ‘휴식시간 자율성’, ‘성장’, ‘조직분위기’, ‘비교’, ‘조직체계’, ‘보수만족 및 업무강도’, ‘조직지원인식’, ‘직무특성’으로 명명하였다. LSA를 통해 추출된 토픽과 이직의 연관성은 일반화가법모형(Generalized Additive Model; GAM)을 이용해 분석하였다. 그 결과 이직과 연관된 토픽 중 선행문헌에서 연구된 개념들과 구별되는 ‘휴식시간자율성’이 추출되었으며, 이러한 결과를 바탕으로 본 연구의 의의 및 한계, 추후 연구 방향등을 논의하였다." @default.
• W4285024279 created "2022-07-12" @default.
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• W4285024279 date "2022-06-25" @default.
• W4285024279 modified "2023-09-30" @default.
• W4285024279 title "Text Big Data Study on Extracting Topics from the Employee Reviews: Focusing on Exploring the Association with Job Turnover" @default.
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• W4285024279 doi "https://doi.org/10.22257/kjp.2022.6.41.2.163" @default.
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