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