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- ## LSTM
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- ### test1
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- - Precision: 0.2195
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- - Recall: 0.3333
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- - F1: 0.2647
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- - Accuracy: 0.6585
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- - Confusion matrix: [[0, 165, 0], [0, 430, 0], [0, 58, 0]]
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- Full classification report:
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- precision recall f1-score support
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-
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- positive 0.0000 0.0000 0.0000 165
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- neutral 0.6585 1.0000 0.7941 430
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- negative 0.0000 0.0000 0.0000 58
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-
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- accuracy 0.6585 653
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- macro avg 0.2195 0.3333 0.2647 653
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- weighted avg 0.4336 0.6585 0.5229 653
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-
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-
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- ### test2
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- - Precision: 0.1939
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- - Recall: 0.3333
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- - F1: 0.2452
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- - Accuracy: 0.5816
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- - Confusion matrix: [[0, 216, 0], [0, 431, 0], [0, 94, 0]]
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-
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- Full classification report:
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- precision recall f1-score support
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-
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- positive 0.0000 0.0000 0.0000 216
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- neutral 0.5816 1.0000 0.7355 431
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- negative 0.0000 0.0000 0.0000 94
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-
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- accuracy 0.5816 741
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- macro avg 0.1939 0.3333 0.2452 741
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- weighted avg 0.3383 0.5816 0.4278 741
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-
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- ### test3
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- - Precision: 0.1106
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- - Recall: 0.3333
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- - F1: 0.1660
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- - Accuracy: 0.3317
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- - Confusion matrix: [[0, 267, 0], [0, 263, 0], [0, 263, 0]]
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-
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- Full classification report:
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- precision recall f1-score support
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-
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- positive 0.0000 0.0000 0.0000 267
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- neutral 0.3317 1.0000 0.4981 263
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- negative 0.0000 0.0000 0.0000 263
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- accuracy 0.3317 793
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- macro avg 0.1106 0.3333 0.1660 793
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- weighted avg 0.1100 0.3317 0.1652 793
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- ## GRU
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-
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- ### test1
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- - Precision: 0.4470
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- - Recall: 0.4538
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- - F1: 0.4485
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- - Accuracy: 0.6064
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- - Confusion matrix: [[86, 69, 10], [97, 302, 31], [16, 34, 8]]
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-
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- Full classification report:
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- precision recall f1-score support
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-
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- positive 0.4322 0.5212 0.4725 165
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- neutral 0.7457 0.7023 0.7234 430
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- negative 0.1633 0.1379 0.1495 58
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- accuracy 0.6064 653
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- macro avg 0.4470 0.4538 0.4485 653
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- weighted avg 0.6147 0.6064 0.6090 653
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- ### test2
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- - Precision: 0.8557
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- - Recall: 0.8500
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- - F1: 0.8527
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- - Accuracy: 0.8880
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- - Confusion matrix: [[191, 19, 6], [20, 397, 14], [9, 15, 70]]
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- Full classification report:
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- precision recall f1-score support
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- positive 0.8682 0.8843 0.8761 216
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- neutral 0.9211 0.9211 0.9211 431
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- negative 0.7778 0.7447 0.7609 94
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- accuracy 0.8880 741
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- macro avg 0.8557 0.8500 0.8527 741
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- weighted avg 0.8875 0.8880 0.8877 741
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- ### test3
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- - Precision: 0.6896
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- - Recall: 0.6454
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- - F1: 0.6251
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- - Accuracy: 0.6456
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- - Confusion matrix: [[187, 58, 22], [21, 237, 5], [41, 134, 88]]
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- Full classification report:
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- precision recall f1-score support
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- positive 0.7510 0.7004 0.7248 267
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- neutral 0.5524 0.9011 0.6850 263
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- negative 0.7652 0.3346 0.4656 263
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- accuracy 0.6456 793
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- macro avg 0.6896 0.6454 0.6251 793
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- weighted avg 0.6899 0.6456 0.6256 793
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- ## CNN
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- ### test1
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- - Precision: 0.6103
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- - Recall: 0.4595
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- - F1: 0.4816
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- - Accuracy: 0.6692
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- - Confusion matrix: [[61, 103, 1], [59, 367, 4], [11, 38, 9]]
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- Full classification report:
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- precision recall f1-score support
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- positive 0.4656 0.3697 0.4122 165
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- neutral 0.7224 0.8535 0.7825 430
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- negative 0.6429 0.1552 0.2500 58
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- accuracy 0.6692 653
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- macro avg 0.6103 0.4595 0.4816 653
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- weighted avg 0.6505 0.6692 0.6416 653
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- ### test2
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- - Precision: 0.9077
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- - Recall: 0.8366
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- - F1: 0.8659
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- - Accuracy: 0.8988
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- - Confusion matrix: [[180, 33, 3], [9, 420, 2], [11, 17, 66]]
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- Full classification report:
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- precision recall f1-score support
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- positive 0.9000 0.8333 0.8654 216
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- neutral 0.8936 0.9745 0.9323 431
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- negative 0.9296 0.7021 0.8000 94
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- accuracy 0.8988 741
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- macro avg 0.9077 0.8366 0.8659 741
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- weighted avg 0.9000 0.8988 0.8960 741
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- ### test3
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- - Precision: 0.7336
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- - Recall: 0.5839
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- - F1: 0.5465
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- - Accuracy: 0.5839
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- - Confusion matrix: [[152, 109, 6], [5, 258, 0], [25, 185, 53]]
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- Full classification report:
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- precision recall f1-score support
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- positive 0.8352 0.5693 0.6771 267
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- neutral 0.4674 0.9810 0.6331 263
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- negative 0.8983 0.2015 0.3292 263
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- accuracy 0.5839 793
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- macro avg 0.7336 0.5839 0.5465 793
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- weighted avg 0.7341 0.5839 0.5471 793
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