Doğal Dil İşleme ile Arapça ve İngilizcede Duygu Analizi
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2024
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Bu çalışmada, denetimli makine öğrenimi yaklaşımını kullanarak İngilizce ve Arapça duygu analizi sorununu ele alıyoruz ve bu kapsamda şu genel adımları izliyoruz: veri toplama, işleme, özellik çıkarma ve duygu sınıflandırma. Çalışmada SemEval18: görev task 1'in İngilizce ve Arapça iki versiyonu ve ArabicNewsHeadline veri seti olmak üzere üç veri seti kullanıldı. Bu very setlerine toplamda 7 makine öğrenme algoritması uygulandı: NB, SVM, KNN, RFC, LGR, QDA ve SGD.
In previous research, the Arabic-language emotion text classification received less attention than the English-language emotion text classification. Therefore, this study addresses the problem of emotion analysis in English and Arabic using the supervised machine learning approach. The subsequent steps were followed: data collection, processing, feature extraction, and emotion classification. Three datasets were used, SemEval18: task 1(in two versions), the English and Arabic datasets, and the ArabicNewsHeadline dataset. Seven machine learning algorithms were applied: NB, SVM, KNN, RFC, LGR, QDA, and SGD.
In previous research, the Arabic-language emotion text classification received less attention than the English-language emotion text classification. Therefore, this study addresses the problem of emotion analysis in English and Arabic using the supervised machine learning approach. The subsequent steps were followed: data collection, processing, feature extraction, and emotion classification. Three datasets were used, SemEval18: task 1(in two versions), the English and Arabic datasets, and the ArabicNewsHeadline dataset. Seven machine learning algorithms were applied: NB, SVM, KNN, RFC, LGR, QDA, and SGD.
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Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Bilgisayar yazılımları, Doğal dil işleme, Duygu analizi, Computer Engineering and Computer Science and Control, Computer softwares, Natural language processing, Sentiment analysis
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213