# Meta-Learner for Amharic Sentiment Classification
## 3 Materials and Methods  
### Overview  
A method of combining machine learning classifiers is called ensemble learning (or ensemble method). Ensemble learning is a technique for combining various base learners from which a new classifier is created [22]. The new classifier is supposed to gain performance compared to any of its constituent base learners. In ensemble learning, differ \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Paper** & **Year Ensemble Approach** & **Average Accuracy/F1-Score** & **Languages** & **Domain/Dataset** \\ \hline
[36] & 2013 Ensemble methods of three base learners - With lexicon features, the ensemble (macro F1 of Arabic & customers’ reviews datasets \\  & (SVM, NB, Rocchio) & 90.95\%) & \\
[32] & 2020 Ensemble-based: GBSVM which com- With TF features, GBSVM outperformed with English & 64,295 Google App user reviews \\  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table} 
Table 1: Summary of key related work of ensemble-based sentiment classification. ent base learners of the same or heterogeneous types are combined using different fusing strategies. Combining classifiers (i.e., either homogeneous or heterogeneous type) in either sequential (i.e., boosting) or in parallel (i.e., bagging) configurations aims to achieve improved classification/regression performance than the performance of individual models. Besides, the other objective of the ensemble method is to reduce variance and bias. That is, ensemble classifiers are not only designed to get a model that achieves performance gains, but also a model that can generalize well. The most popular ensemble approaches: voting, bagging, boosting, and stacking [22; 42; 43]. Voting is the simplest of all fusing strategies, which takes the most frequent predicted class of multiple predictors. For sample \(x\), class \(i\) is assigned if class \(i\) is predicted most frequently. Mathematically,  
\[c_{i}=mode\{h_{1}(x);h_{2}(x);\ldots;h_{N}(x)\}. \tag{1}\]  
where \(c_{i}\) is the predicted class, mode is statistical mode of prediction by classifiers \(h_{1},h_{2},\ldots h_{N}\) for sample \(x\). Bagging is a method for creating \(N\) base learners by corresponding \(N\) sample data generated randomly from the training dataset (with replacement). The bagging method is also called Bootstrap aggregation. Suppose from \(N\) iterations, \(N\) random samples from training data are generated. For \(N\) base learners, the final prediction of their ensemble is given by averaging all predictions from all \(N\) models using Equation (2). \[c_{i}=\frac{\sum_{j=1}^{N}(h_{j}(x)=i)}{N}. \tag{2}\]  
Popular bagging algorithms are random forest and bagging meta-estimators, just to name a few. Boosting is an ensemble of base learners in a sequence where each classifier is started with equal weight, but after all models are trained once, weight is assigned to each model based on its performance. After model evaluations, a larger weight is assigned to a misclassified sample for providing greater focus in the next iteration, and vice versa. The final model relies on a weighted averaging method. Mathematically,  
\[c_{i}=\frac{\sum_{i=1}^{N}\frac{(h_{1}w_{i})}{\sum w_{i}}}{N}. \tag{3}\]  
where \(h_{1},h_{2},\ldots h_{N}\) are base learners, \(w_{1},w_{2},\ldots w_{N}\) are weights, \(N\) is the number of classifiers, and \(h\) is the final classifier. It is noted that boosting considers weighting in training data which is one of the feature making it different from bagging. Boosting classifiers include gradient boosting classifier, adaboost classifier, extreme gradient boosting classifier, and light gradient boosting classifiers.