| ## 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 | |