## 3 Proposed Models
### Base learners and Meta-Learner  
The classic machine learning techniques, like Logistic Regression [55] and Support Vector Machines [56], are used to train datasets that are comprised of features of'surface feature', which are \(\text{D}_{\text{negation}}\), \(\text{D}_{\text{contrast}}\), \(\text{D}_{\text{inconsistency}}\), \(\text{D}_{\text{no\_shift}}\), and \(\text{D}_{\text{full}}\). They are the highly estimated techniques for text classifying, in general, and sentiment classifying, in particular. Along with them, a deep learning model is chosen to train all the datasets (\(\text{D}_{\text{full}}\)) for the purpose of identifying features of 'deep feature' for ensemble learning. Base learners' output values are each sample's probability of belonging to negative and positive stages. These probabilities are used as intensifying learning data for the ensemble stage. Regarding ensemble learning, here are the two models used to blend base-classifiers' results [57]: (1) Rule Fixed model uses fixed rules to choose inputs for ensemble learning, and the majority of ensemble learning's results are based on classifier output's results; (2) Meta-Classifier model found in classifiers' results is taken for ensemble learning model's features. In this paper, we used the Meta-Classifier model with Logistic Regression technique. Figure 4 describes the architecture of ensemble learning using the Meta-Classifier model with base learners' output results, which are each sample's probability of belonging to positive and negative stages. The whole process can be described in Algorithm 1. Figure 4: Ensemble learning using Meta-Classifier model. Figure 3: Process identifying ‘deep features’. ``` Input:
* negation indicator \(N=\{n_{1},n_{2},\ldots\ n_{s}\}\);
- contrast indicator \(C=\{c_{1},c_{2},\ldots\ c_{t}\}\);
- a base learner algorithm: \(L\);
- a deep learning model: \(DL\); Process: Step 1. Identifying these sub datasets: \(D_{negation}\), \(D_{contrast}\), \(D_{inconsistency}\), \(D_{no-high}\): for \(k=1,\ldots,n\): for \(i=1,\ldots,n\): for \(j=1,\ldots,|s_{i}|\): if \(w_{ij}\in N\): put \(s_{i}\) into \(d_{k-negation}\); # capture negations continue; if \(w_{ij}\in C_{1}\): put \(s_{i-1}\) into \(d_{k-contrast}\); # capture fore-contrast (\(C_{1}\)) continue; if \(w_{ij}\in C_{2}\): put \(s_{i}\) into

**Models contributing to experiment process:**

We compared our model with other strategies, the traditional SVM classification method, along with the deep learning model LSTM, as follows:

* SVM: sentiment classification using the classic machine learning method, Support Vector Machines, with the bag-of-words model, unigram feature. * LSTM: sentiment classification using Long Short-Term Memory \(2\times 64\) hidden-layer units with feature representing the Word2 Vec type. The original dimension of our one-hot vector is 74,268, reduced to 300 after performing word embedding. Dropout and recurrent dropout are 0.5. Activation function is sigmoid. * Classifier ensemble model (CEM)(4C-LR): model comprised of meta-learner using Logistic Regression and four base learners which are contrast learner, inconsistency learner, negation learner, and no_shift learner. * CEM(5C-LR): model comprised 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 comprised of meta-learner using Logistic Regression and six base learners, which are contrast learner, inconsistency learner, negation learner, no-shift learner, full learner, and LSTM learner. With \"contrast\", \"inconsistency\", \"negation\", \"no-shift\", and \"full\" are the names of sub-datasets, which are described in Section 3.2.1, the Logistic Regression technique is adopted for all base learners. **Experiment results:**

Experiment results calculated according to accuracy ratio are shown in Table 6 and Figures 5 and 6. ### English Language

**Dataset**: We experimented on corpus proposed by [PERSON] et al.