이전
Learning 함수에서 학습한 ANN 모델로 새로운 메시지 감정분류(긍/부정, 희/노/애/락 등)에 대한 예측치와 예측력을 구하는 함수
LearningWeight,tags_2gram.csv
clean_test_message.csv
prediction_results.csv
- Reference file: (1) 아래 표처럼 Learning 함수에서와 동일한 tag 목록, 그리고 (2) Learning 함수의 output을 콤마(,)를 구분자로 하여 받아옴
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
- Input file: document 컬럼에 새로 감정 분류 라벨링을 하고자 하는 정제 텍스트가 있는 CSV 파일
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
- Output file: 새로운 input 메시지에 대한 모델 예측치와 예측력 (1gram용 함수와 output format 동일)