A review of sentiment analysis for Afaan Oromo: Current trends and future perspectives

Abstract

Sentiment analysis, commonly referred to as opinion mining, is a fast-expanding area that seeks to ascertain the sentiment expressed in textual data. While sentiment analysis has been extensively studied for major languages such as English, research focusing on low-resource languages like Afaan Oromo is still limited. This review article surveys the existing techniques and approaches used for sentiment analysis specifically for Afaan Oromo, the widely spoken language in Ethiopia. The review highlights the effectiveness of combining neural network architectures, such as Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) models, as well as clustering techniques like Gaussian Mixture Models (GMM) and Support Vector Machine (SVM) in sentiment analysis for Afaan Oromo. These approaches have demonstrated promising results in various domains, including social media content and SMS texts. However, the lack of a standardized corpus for Afaan Oromo NLP tasks remains a major challenge, which indicates the need for comprehensive data collection and preparation. Additionally, challenges related to domain-specific language, informal expressions, and context-specific polarity orientations pose difficulties for sentiment analysis in Afaan Oromo.