Studying the specific subject of opinion mining has been a popular research area as a means of overcoming the challenge of user-generated content on the web, which can be challenging to manually collect, comprehend, summarize, and analyze for decision-making. Even though there are three various levels at which opinion mining can be done, the detail and complexity of feature level opinion mining outweighs its disadvantages. The goal of this research is to provide sentiment mining and aspect-based opinion summaries of service reviews in Afaan Oromo for Oromia Radio and Television Organization (ORTO). 400 reviews in all were gathered and used for news-related purposes from ORTO. The model has five elements, including document inspection, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summary, as well as a bar chart to show aspect-based sentiment summation. Five different processes make up the model: document review, pre-processing, aspect extraction, polarity detection, and aspect-based sentiment summarization. A bar chart is also utilized to visually depict aspect-based opinion polarity. For positive classes, 90% precision and 87% recall are accomplished, while for negative classes, 87% precision and 89.7% recall are attained. The main issue identified in this study is that users tend to express their opinions in a context-based or indirect manner. They could express their negative feelings with pleasant words or the opposite. Therefore, more research is required before the algorithm will take context-based or semantic opinion mining into account.