--- license: apache-2.0 datasets: - ZakyF/PRDECT-ID language: - id metrics: - accuracy evaluation: - task: type: text-classification name: Sentiment Analysis metrics: - name: Accuracy type: accuracy value: 1.0 - name: Cross-Validation Accuracy type: accuracy value: 0.99981 pipeline_tag: text-classification library_name: sklearn tags: - sentiment-analysis - nlp - naive-bayes - e-commerce - indonesian --- # Sentiment Analysis Model SVM dan Naive Bayes untuk mengklasifikasikan ulasan ke dalam kategori Bagus, Normal, atau Buruk menggunakan PRDECT-ID Dataset. ## Deskripsi Model ini menganalisis ulasan pelanggan Tokopedia untuk menghasilkan insight seperti rekomendasi perbaikan pengiriman atau kualitas produk. ## Penggunaan ```python import pickle from sklearn.preprocessing import LabelEncoder, StandardScaler # Load model dan preprocessing svm_model = pickle.load(open('svm_model.pkl', 'rb')) scaler = pickle.load(open('scaler.pkl', 'rb')) le_sentiment = pickle.load(open('le_sentiment.pkl', 'rb')) le_emotion = pickle.load(open('le_emotion.pkl', 'rb')) # Contoh prediksi data = [[5, 'Positive', 'Happy']] # Rating, Sentiment, Emotion data_scaled = scaler.transform(data) prediksi = svm_model.predict(data_scaled) print(prediksi) # Output: ['Bagus']