ArticleComputational intelligence and neuroscience2021
Automated Amharic News Categorization Using Deep Learning Models.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Enhancing amharic news classification through ontology-based feature fusion and logistic regression.Scientific reports · 2026Article
- Hybrid attention-based multi-class classification of Ethiopian legal texts using deep learning.PloS one · 2026Article
- Public opinion mining in social media about Ethiopian broadcasts using deep learning.Scientific reports · 2024Article
- AI-based disease category prediction model using symptoms from low-resource Ethiopian language: Afaan Oromo text.Scientific reports · 2024Article
- Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model.Computational intelligence and neuroscience · 2022Article
- Feature selection by integrating document frequency with genetic algorithm for Amharic news document classification.PeerJ. Computer science · 2022Article
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Authors and funding
2 authors.
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Abstract
For decades, machine learning techniques have been used to process Amharic texts. The potential application of deep learning on Amharic document classification has not been exploited due to a lack of language resources. In this paper, we present a deep learning model for Amharic news document classification. The proposed model uses fastText to generate text vectors to represent semantic meaning of texts and solve the problem of traditional methods. The text vectors matrix is then fed into the embedding layer of a convolutional neural network (CNN), which automatically extracts features. We conduct experiments on a data set with six news categories, and our approach produced a classification accuracy of 93.79%. We compared our method to well-known machine learning algorithms such as support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), XGBoost (XGB), and random forest (RF) and achieved good results.
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