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Register a new userImproved Text Classification of Long-term Care Materials
تحسين تصنيف النص مواد الرعاية الطويلة الأجل
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وقد شكلت الشيخوخة السكان تحديا للعديد من البلدان بما في ذلك تايوان، ومعهم يأتون قضية الرعاية الطويلة الأجل.بالنظر إلى السياق الحالي، كان الهدف من هذه الدراسة هو استكشاف الفرعي المناقش أعلاه في مجال الرعاية الطويلة الأجل، وتحديد ميزاته من خلال NLP.تقدمت هذه الدراسة TF-IDF، نموذج الانحدار اللوجستي، ومصنف البايز الساذج لمعالجة البيانات.باختصار، أظهرت النتائج أنها وصلت إلى أفضل درجة F1 من 0.920 في تحديد الهوية، وأفضل دقة 0.708 في التصنيف.يمكن استخدام نتائج هذه الدراسة كمرجع للتطبيقات المتعلقة بالرعاية الطويلة الأجل في المستقبل.
Aging populations have posed a challenge to many countries including Taiwan, and with them come the issue of long-term care. Given the current context, the aim of this study was to explore the hotly-discussed subtopics in the field of long-term care, and identify its features through NLP. This study applied TF-IDF, the Logistic Regression model, and the Naive Bayes classifier to process data. In sum, the results showed that it reached a best F1-score of 0.920 in identification, and a best accuracy of 0.708 in classification. The results of this study could be used as a reference for future long-term care related applications.
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