Advances in internet and web technologies have brought diversified online platforms for people to engage in online chats, discussions, and access instant information delivered to their handheld devices. Social media platforms, such as Facebook and X, have played a paramount role by enabling people to share their ideas, emotions, feelings, or talk about anything that affects us one way or another. This study introduces a machine learning-based Amharic language Facebook sentiment classifier model designed to conduct sentiment analysis for the Ethiopian electric utility’s electric distribution service. In this sentiment classification experiment, one classical machine learning and three deep learning models were evaluated: Support Vector Machine (SVM), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (Bi-LSTM). The performance evaluation of these models reveals distinct patterns in their accuracy rankings based on test outcomes. For multiclass classification, the SVM model achieved test accuracy of 74.8%, and in binary class classification, it demonstrated an impressive test accuracy of 89.18%. Following closely, the Bi-LSTM model emerged as a strong contender with test accuracy slightly lower at 73.55% in multi-class classification and a commendable test accuracy of 87% in binary class classification. The LSTM model showcased a test accuracy of 72.75% in multi-class classification and a commendable test accuracy of 87% in binary class classification. Lastly, the RNN model exhibited a test accuracy of 65.9% in multi-class classification, and in binary class classification, the test accuracy slightly dipped to 86.33%.