# Meta-Learner for Amharic Sentiment Classification
## 2 Literature Review
### Ensemble Approaches for Sentiment Classification  
[PERSON] et al. [32] proposed an ensemble learning that combines gradient boosting and support vector machines using voting classifier. With term-frequency and TF-IDF (uni-, bi-, and tri-gram) features, the proposed approach is evaluated on a dataset of 64,295 Google App user reviews, which are labeled either positive, negative or neutral. Their approach has achieved the highest accuracy of 0.93 with term-frequency feature compared to the other variants of TF-IDF features and compared to individual classifiers. The authors did not mention why they chose TF-IDF word \(n\)-grams over TF-IDF character \(n\)-grams. In similar research, [PERSON] et al. [33] assessed and compared three most popular ensemble approaches (i.e., Bagging, Boosting, and Random Subspace) relying on base learners (i.e., Naive Bayesian (NB), Maximum Entropy, Decision Tree, K-Nearest Neighbor (KNN), and SVM) for sentiment classification using BoW features of ten publicly available datasets. With 1200 comparative group experiments, the results have shown that ensemble learners achieved performance gains over the base learners for sentiment classification. In contrast to the work in [32], the author of [33] selected BOW features over TF-IDF term features. In other work, [PERSON] et al. [34] developed an ensemble of NB, SVM, Bayesian Net, C4.5, and RF using majority voting for sentiment classification of Airline customer service feedback. The proposed ensemble outperformed with F-score of 84.2% for three class datasets and 91.7% for two class datasets using 10 folds CV of 12,864 tweets. Yet another similar approach was done by [PERSON] et al. [35] who proposed an ensemble approach which improved sentiment classification relying on base learners (SVM, NB, LR, and RF) using seven lexical resources. For training, the base learners used sentiment lexicon features from the three tweeter datasets. The performance of the proposed ensemble learning was better than the individual learners. However, the proposed ensemble (i.e., accuracy of 81.0%) has no significant difference in performing the RF (i.e., accuracy of 82.4%). In [36], [PERSON] et al. proposed ensemble methods for the problem of subjectivity and sentiment classification of Arabic texts by combining base learners, Rochio, SVM, and NB. The results show that the ensemble classifier performed better than base learners' performance. [PERSON] et al. in [37] built combined two approaches (lexicon-based and Machine Learning (ML) that uses SVM) based on weighted voting meta-classifier. Machine learning with SVM outperformed the lexicon-based method. The author recommended that combining multiple classifiers using the meta-classifier scheme could enhance the performance of sentiment classification. In a similar study, [PERSON] et al. [38] proposed stacking ensemble model for feature-based sentiment analysis by combining SVM, NB, and KNN as base learners and SVM as meta classifier. Using the Laptop product review dataset (44 features of 4096 laptop reviews), the stack model achieved an accuracy of 92.5%. In another research, [PERSON] et al. [39] developed a bootstrap ensemble framework for sentiment classification of English Tweets. The intention of the proposed framework is to tackle the challenges of tweeter sentiment classification, such as class imbalance, sparsity, and representational richness issues. In the framework, first text features are extracted, then seven different machine learning algorithms are trained, finally selected models are combined. This proposed approachhas shown more accurate and balanced prediction of sentiment of tweeter compared to other algorithms. Yet another similar approach was done by [PERSON] et al. [40], which integrated two or more unsupervised approaches using meta-classifiers for Spanish movie review sentiment classification. Stacking classifiers (SVM, Naive Bayesian, and Logistic Regression) were used as the meta-learners to combine the unsupervised models.