ASPECT BASED SENTIMENT ANALYSIS FOR AFAAN OROMOO TEXT USING BERT

Abstract

Aspect-based sentiment analysis (ABSA) is a more important and advanced task of sentiment analysis which determine both the sentiments and the aspects within the text. It is an essential research field within natural language processing, especially for languages that lack extensive resources. This study focuses on developing an ABSA model for Afaan Oromoo language, one of the widely spoken languages in Ethiopia. Despite the rich linguistic diversity of Afaan Oromoo, there is a scarcity of computational tools and datasets for sentiment analysis in this language. Our research addresses this gap by creating a comprehensive dataset annotated with BIO annotation scheme for aspect terms and integrates CNN and BiLSTM for aspect extraction, and BERT for aspect sentiment classification. We fine-tuned pre-trained BERT model on our annotated Afaan Oromoo dataset to perform aspect based sentiment analysis. The total of 2550 review text collected from FBC Afaan Oromoo Facebook page, BBC Afaan Oromoo and other relevant social media are used for this study. After data collection, two annotators’ annotated data manually into three classes (i.e., positive, negative and neutral). The aspect terms used for study are extracted from three domain, coffee, gold and flower. Basically ten aspect terms namely (qulqullinna bunaa, oomisha bunaa, foolii, dandhama, worqee baasuu, galii, gatii, diinagdee, agarsiisa worqee and al-ergii) are used for the study. CNN-BiLSTM is used for aspect extraction and performed 92.8% of accuracy. BERT model performed accuracy of 87% for aspect sentiment classification. This work not only contributes to the development of sentiment analysis for Afaan Oromoo but also provides a framework for applying advanced NLP techniques to other low-resource languages