Advances, Systems and Applications
Table 5 Comparison among our proposed model and previous works
From: ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing
Author | Accuracy% | Method Description |
|---|---|---|
Pang and Lee[27] | 87.20% | Subjective summarization based on minimum cuts |
Kennedy and Inkpen [28] | 86.20% | Contextual Valence Shifters |
Martineau and Finin [29] | 88.10% | TFIDF Weighting |
Maas et al. [30] | 88.90% | A mix of unsupervised and supervised techniques to learn word vectors |
Tu et al.[31] | 88.50% | Word embedding using vector kernel + tree-based word dependency integrated with grammar relations |
Nguyen et al.[32] | 87.95% | Two-step classification of support vector machine and Naive Bayes |
Our proposed model | 89.02% | The combination of the convolution layer and the LSTM layer and the convolution layer (ConvLSTMConv) |