# Meta-Learner for Amharic Sentiment Classification
###### Abstract
The nature of the labeled data (such as its quality, whether it is balanced, and the type of extracted features) affects the performance of machine learning. The machine learning algorithms are assumed to properly discriminate the target sentiment classes. There are few Amharic sentiment analysis works; they are categorized into lexicon-based [6, 7, 8, 9], machine learning-based approaches (SVM, RF,NB) [10, 11, 12, 13], deep learning approaches (LSTM and CNN) [14, 15], and those that use BERT [16]. There are many sentiment analysis research works in English and many non-English languages. Several approaches trying to transfer sentiment information from high- resourced languages such as English to other languages have been proposed. For example, [PERSON] et al. [17] exploited three English lexical resources\(-\)MPQA, Appraisal, and SentiWordnet\(-\)by translating them into three Chinese Sentiment Lexions using Google translator. The performances of these generated Chinese lexicons were compared with the Chinese NTUSD sentiment lexicon by implementing them to classify sentiment on Chinese product review using SVM and Naive Bayesian classifiers. Here, in this study, we proposed a combination of machine learning approaches (i.e., ensemble learning). Ensemble learning is a technique for combining various base learners from which a new classifier is created. The new classifier is supposed to gain performance compared to any of its constituent base learners [18]. In ensemble learning, different base learners of the same or heterogeneous types are combined using different fusing strategies (i.e., voting, averaging, and stacking) [19, 20, 21]. This work addresses the next four research questions: (1) To what extent does ensemble learning improve Amharic sentiment classification on small set of user generated texts compared to base learners? (2) Which feature representation (TF-IDF uni-gram, TF-IDF character \(n\)-grams) has better performance of Amharic sentiment classification with the proposed ensemble approach? (3) Does the SMOTE technique improve performance of the proposed approach by balancing the imbalanced labeled user-generated data? (4) Onwhich feature representation of Amharic texts does SMOTE show higher performance improvement of sentiment classification? The aim of this paper is to investigate the text features along with the effect of SMOTE techniques for Amharic sentiment classification on four sentiment-labeled user-generated data sets and to test to what extent ensemble learning is improving sentiment classification as compared to base learners. The key contributions of this research is as follows. * The provision of an annotated data sets [22]. * The effect of SMOTE with TF-IDF character \(n\)-grams feature is tested on Amharic sentiment classification by using ensemble learning. * SMOTE with TF-IDF character \(n\)-grams feature works better than the one with (or without) SMOTE on performance of Amharic sentiment classification of user generated text. * SMOTE with TF-IDF word uni-gram has shown performance gains of sentiment classification as compared to TF-IDF uni-gram with no SMOTE and
* TF-IDF character (1,7) grams is found to be the most salient feature for discriminating sentiment categories of Amharic user-generated text. The paper is organized as follows. Section 2 surveys the related works dealing with ensemble learning for sentiment classification. Section 3 describes the materials and methods, which further describe the fusion methods of ensemble learning and followed by specifying the workflow of the proposed stacking approach. Section 4 reports the results of experiments which are carried out to evaluate the proposed ensemble learning to use different Amharic user generated data sets. The last section presents the conclusion drawn from the results of the research.