This paper presents a hybrid methodology for Turkish sentiment analysis, which combines the lexicon-basedand machine learning (ML)-based approaches. On the lexicon-based side, we use a sentiment dictionary that is extendedwith a synonyms lexicon. Besides this, we tackle the classification problem with three supervised classifiers, naive Bayes,support vector machines, and J48, on the ML side. Our hybrid methodology combines these two approaches by generatinga new lexicon-based value according to our feature generation algorithm and feeds it as one of the features to machinelearning classifiers. Despite the linguistic challenges caused by the morphological structure of Turkish, the experimentalresults show that it improves the accuracy by 7% on average.