Delete results(test1,2,3).md

results(test1,2,3).md

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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
-
-