Sentiment analysis (SA) is the process of assessing the sentiment and attitude of digital audiences toward a range of topics and subjects. The aim of this research is to propose an effective approach for finding good-quality solutions for dialectal Arabic SA problems by addressing inherent challenges in an optimal way. This is achieved by determining the polarities of review texts by using the k-means clustering algorithm in a lexicon-based model and also applying a ML model where necessary in a hybrid approach. In this research, a sentiment lexicon (senti-lexicon) corpus of 3,824 positive and negative words/terms is used in a deep feature extraction process to convert the text into feature vectors. The experimental results showed that the k-means clustering model worked better after separating the observations with relative score values and moving them to be classified using the lexicon-based model. The k-means clustering model part of the hybrid model yielded high-performance results in terms of accuracy, recall, and F1 score metrics, especially in the positive and negative score value features and total score. Each technique has shortcomings, the hybrid model; as the results that are shared will represent; prove that it is an ideal and more flexible solution and approach to conducting SA in an effective and self-improving manner.