Next Article in Journal
The Effects of Chewing Exercises on Masticatory Function after Surgical Orthodontic Treatment
Previous Article in Journal
Vitamin C-Assisted Fabrication of Aerogels from Industrial Graphene Oxide for Gaseous Hexamethyldisiloxane Adsorption
 
 
Article

Meta-Learner for Amharic Sentiment Classification

by 1,2,*, 3,† and 4,†
1
IT Doctoral Program, Addis Ababa University, Addis Ababa P.O. Box 28762, Ethiopia
2
Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 16417, Ethiopia
3
Institute of Information Systems Engineering, Technical University of Vienna, Favoritenstraße 9-11/194-01, A-1040 Vienna, Austria
4
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; https://doi.org/10.3390/app11188489
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
Show Figures

Figure 1

MDPI and ACS Style

Neshir, G.; Rauber, A.; Atnafu, S. Meta-Learner for Amharic Sentiment Classification. Appl. Sci. 2021, 11, 8489. https://doi.org/10.3390/app11188489

AMA Style

Neshir G, Rauber A, Atnafu S. Meta-Learner for Amharic Sentiment Classification. Applied Sciences. 2021; 11(18):8489. https://doi.org/10.3390/app11188489

Chicago/Turabian Style

Neshir, Girma, Andreas Rauber, and Solomon Atnafu. 2021. "Meta-Learner for Amharic Sentiment Classification" Applied Sciences 11, no. 18: 8489. https://doi.org/10.3390/app11188489

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop