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Meta-Learner for Amharic Sentiment Classification

by 1,2,*, 3,† and 4,†
IT Doctoral Program, Addis Ababa University, Addis Ababa P.O. Box 28762, Ethiopia
Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 16417, Ethiopia
Institute of Information Systems Engineering, Technical University of Vienna, Favoritenstraße 9-11/194-01, A-1040 Vienna, Austria
Department of Computer Science, Addis Ababa University, Addis Ababa P.O. Box 1176, Ethiopia
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2021, 11(18), 8489;
Received: 5 August 2021 / Revised: 1 September 2021 / Accepted: 5 September 2021 / Published: 13 September 2021
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. View Full-Text
Keywords: ensemble learning; Amharic sentiment classification; stacking; meta-learner; character n-grams ensemble learning; Amharic sentiment classification; stacking; meta-learner; character n-grams
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MDPI and ACS Style

Neshir, G.; Rauber, A.; Atnafu, S. Meta-Learner for Amharic Sentiment Classification. Appl. Sci. 2021, 11, 8489.

AMA Style

Neshir G, Rauber A, Atnafu S. Meta-Learner for Amharic Sentiment Classification. Applied Sciences. 2021; 11(18):8489.

Chicago/Turabian Style

Neshir, Girma, Andreas Rauber, and Solomon Atnafu. 2021. "Meta-Learner for Amharic Sentiment Classification" Applied Sciences 11, no. 18: 8489.

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