## 3 Proposed Models
### Base learners and Meta-Learner
[59] including four domains of Electronics, DVD, Books, and Kitchen, each of which contained 1000 reviews labeled positive and 1000 reviews negative. These datasets were used for fair comparison purposes since two other approaches were used, as shown below. We proportioned their train-test as 90-10%. **Models contributing to experiment process:**

Similar to the Vietnamese language, the following models contributed to experiment process:

* SVM: sentiment classification using the classic machine learning method Support Vector Machines with the bag-of-words model, unigram feature. * MLP (Multilayer Perceptron): sentiment classification using neural network with 160 inputs, \(2\times 50\) hidden-layer neurons, and 2 outputs. Activation function is softmax. * PSDEE: method proposed by [PERSON] et al. [5] using four sub-datasets of contrast, inconsistency, negation, and no_shift with unigram feature. There are four base learners for training and combining tasks. * LSS: method proposed by [PERSON] et al. [43] using two sub-datasets of shift and no_shift with unigram feature. There are two base learners for training and combining tasks. * CEM(5C-LR): model comprising of meta-learner using Logistic Regression and five base learners which are contrast learner, inconsistency learner, negation learner, no_shift learner, and full learner. * CEM(6C-LR)--the proposed model: model comprising of meta-learner using Logistic Regression and six base learners which are contrast learner, inconsistency learner, negation learner, no_shift learner, full learner, and MLP learner. **Experiment results:**

Experiment results, which were calculated according to accuracy ratio are shown in Table 7 and Figures 7-10. Figure 8: Results experimented on corpus DVD. Figure 7: Results experimented on corpus Electronics. Figure 9: Results experimented on corpus Books. ### Evaluation

Based on the experiment results for the Vietnamese language, we have come to the following conclusions:

* Models interested in 'deep features', such as the CEM(6C-LR), leads to results better than other models in both datasets experimented, especially compared with other strategies of four base learners, five base learners, and the baseline machine learning method SVM. * Ensemble learning of training sets containing 'deep feature' and features relevant to polarity shifting of'surface feature' type leads to classification results better than the deep learning state-of-the-art model in sentiment classification (LSTM). The 'deep features' were the most effective features used to classify sentiment in an ensemble system. * Dataset size also has an effect on every method's effectiveness. With limited data (HOTEL-Reviews), SVM always proves to be an effective text classification method when compared with LSTM or the model proposed by us (CEM(6C-LR)). Our dataset size in the English language is limited, and, therefore, we have chosen the neural multilayer network (MLP) model instead of LSTM. It is certain that the original LSTM suffers from poor performance when applied with a small dataset. Experiment results show that the model proposed by us, CEM(6C-LR), attains higher classification effectiveness than other methods, especially when compared with the PSDEE method given by [PERSON] et al. [5] and the LSS method proposed by [PERSON] et al. [43]. Figures 11 and 12 show the average accuracy of each base learner in the method of [PERSON] et al. [5] and in the proposed model. The [PERSON] et al. method used a statistical approach to predict data test labels in the inconsistency learner and no-shift learner. The average accuracy of the two learners was only 70%, with a final accuracy of 82.43%. The proposed model used the baseline method for the inconsistency learner and no-shift learner, which provided an average accuracy of 76%. By adding a deep learning learner, the proposed system achieved a final accuracy of 84.62%.