Table 5 Precision of the proposed and existing classifiers with term weights
From: Twitter sentiment analysis using hybrid gated attention recurrent network
Classifiers | Term weighting-based feature extraction schemes | ||||
|---|---|---|---|---|---|
LTF-MICF % | TF-DFS % | TF-IDF % | TF % | W2V % | |
Proposed GARN | 96.65 | 96.03 | 95.67 | 94.90 | 93.90 |
Bi-GRU | 95.22 | 94.51 | 94.12 | 93.76 | 93.59 |
Bi-LSTM | 93.61 | 93.34 | 92.82 | 92.37 | 91.99 |
RNN | 91.11 | 90.65 | 89.74 | 89.38 | 88.28 |
CNN | 86.83 | 85.41 | 84.17 | 82.84 | 80.63 |