## 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