Figure 1:
Methodology of reductive bias in gated recurrent SA of twitter data. RD-GRU, reductive bias-based gated recurrent unit; SA, sentiment analysis.
Figure 2:
Block diagram for SA for social media texts. LTF-MICF, log term frequency-modified inverse class frequency; SA, sentiment analysis.
Table 1:
The six attributes of Sentiment140 dataset
| S. no. | Attributes |
|---|---|
| 1. | Text of the tweet |
| 2. | User who tweeted |
| 3. | Whether there is a query on the tweet or not? |
| 4. | Date of tweet |
| 5. | Tweet ID |
| 6. | Polarity of the tweet |
Table 2.
Performance analysis of the proposed method for Sentiment140 dataset
| Methods | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) |
|---|---|---|---|---|
| CNN | 91.11 | 92.93 | 89.96 | 91.42 |
| LSTM | 93.79 | 94.41 | 91.40 | 92.88 |
| RNN | 95.36 | 96.95 | 93.15 | 95.01 |
| GRU | 96.74 | 97.42 | 95.63 | 96.52 |
| Proposed RD-GRU Method | 98.92 | 98.49 | 97.54 | 98.01 |
Table 3.
Performance analysis of feature extraction method for Sentiment140 dataset
| Methods | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) |
|---|---|---|---|---|
| Word2Vec | 91.09 | 92.48 | 93.04 | 92.76 |
| Glove | 94.13 | 95.69 | 95.80 | 95.74 |
| Skip gram | 96.33 | 96.29 | 96.36 | 96.32 |
| LTF-MICF | 98.92 | 98.49 | 97.54 | 98.01 |
Table 4.
Comparative analysis of the proposed method for Sentiment140 dataset
Table 5.
Comparative analysis of the proposed method for Twitter API dataset
| Methods | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) |
|---|---|---|---|---|
| TSA-CNN-AOA (KNN) | 95.09 | 94.23 | 93.23 | 93.71 |
| Proposed RD-GRU | 97.84 | 97.37 | 96.95 | 97.15 |
Figure 3:
Performance analysis of the proposed method for Twitter API dataset. API, CNN, convolutional neural network; GRU, gated recurrent unit; RD-GRU, reductive bias-based gated recurrent unit; RNN, recurrent neural network.
Figure 4:
Performance analysis of feature extraction method for Twitter API dataset. API, LTF-MICF, log term frequency-based modified inverse class frequency.