# Meta-Learner for Amharic Sentiment Classification
###### Abstract  
The emergence of the World Wide Web facilitates the growth of user-generated texts in less-resourced languages. Sentiment analysis of these texts may serve as a key performance indicator of the quality of services delivered by companies and government institutions. The presence of user-generated texts is an opportunity for assisting managers and policy-makers. These texts are used to improve performance and increase the level of customers' satisfaction. Because of this potential, sentiment analysis has been widely researched in the past few years. A plethora of approaches and tools have been developed\(-\)albeit predominantly for well-resourced languages such as English. Resources for less-resourced languages such as, in this paper, Amharic, are much less developed. As a result, it requires cost-effective approaches and massive amounts of annotated training data, calling for different approaches to be applied. This research investigates the performance of a combination of heterogeneous machine learning algorithms (base learners such as SVM, RF, and NB). These models in the framework are fused by a meta-learner (in this case, logistic regression) for Amharic sentiment classification. An annotated corpus is provided for evaluation of the classification framework. The proposed stacked approach applying SMOTE on TF-IDF characters (1,7) grams features has achieved an accuracy of 90%. The overall results of the meta-learner (i.e., stack ensemble) have revealed performance rise over the base learners with TF-IDF character \(n\)-grams. s 202  
activity of a person's judgmental view towards an issue or object. Sentiment is the consciously judgmental view of a person in favor of an issue (i.e., positive sentiment) and/or express judgmental views of a person against an issue (i.e., negative sentiment). The automated analysis of opinionated text is sentiment analysis (also called opinion mining). However, major issues with typical NLP approaches include (i) lack of NLP resources for most languages other than English, (ii) mastering natural language understanding, and (iii) the difficulty of reasoning across large and multiple documents [1]. For cases where the language under consideration is less resourced, these problems are intractable for performing computational linguistic research. Less-resourced language problems are primarily associated with the lack of adequate and efficient tools for the processing of natural language: part-of-speech tagger, stemmer, lemmatizer, dependency parser, named-entity tagger, etc. To resolve these challenges, addressing the lack of annotated corpora is to perform specific linguistic tasks, including development of automatic annotated data, role of expert/linguistic knowledge, develop a single language or universal solutions and/or resource creation [2]. The reports in [3,4] state that half of the world's languages will vanish by the end of this century. Thus, proper attention is needed to the language professionals and the speakers of these languages. For the issues related to insufficiency of linguistic resources, techniques such as creating more labeled NLP corpora and leveraging resource-rich languages are undertaken to ease the scarcity in less resourced languages. Semi-supervised, unsupervised methods, and rule-based methods are the most popular approaches to create the required tools. For the challenges related to scarcity of labeled NLP corpora, some approaches use linguistic knowledge to seed unsupervised models and use this linguistic knowledge to adapt models and approaches of familiar languages. In [5], approaches to less-resourced languages are reviewed and suggested addressing these problems. Amharic is one of the less-resourced languages, which is one of the Semitic languages. Amharic opinionated texts are increasing quickly. However, due to lack of labeled corpus, sentiment lexicon, and other linguistic resources, research works on sentiment classification in this language is challenging.