In this paper, we are interested in developing a sentiment analysis (SA) system on the basis of machine learning (ML) techniques. We propose a Libyan dialect twitter dataset which we have built. It contains 6,000 comments and tweets cleaned, pre-processed, and annotated. To evaluate the performance of sentiment classification of the tweets and comments, four machine learning classifiers have been applied. These results show that all classifiers achieved good results. Furthermore, we have created a basic sentiment lexicon which includes words and phrases with sentiment polarity (positive, negative, and neutral). This sentiment lexicon is a valuable resource for the Libyan dialect.