Sentiment analysis is a task of natural language processing whose goal is to create an automated system that is able to determine the sentiment of texts written in natural language. In this paper, the sentiment analysis was performed on a data set of tweets written in the Croatian language. For each tweet in the data set, its sentiment is manually annotated. Tweets are firstly pre-processed. As a part of pre-processing noise was removed from the tweets, after which they were tokenized and lemmatized. An exploratory analysis of the set of tweets used was then performed, followed by the sentiment classification using machine learning methods. For sentiment classification, the naive Bayes classifier, logistic regression and support vector machine, their voting ensemble and stacking ensemble with logistic regression or support vector machine as the final estimator were trained. The classifiers were trained on tweet data sets in which the emoticons were replaced with appropriate text or were removed. The features from the tweets datasets were extracted using TF-IDF and bag-of-words methods. Obtained results are satisfactory. The accuracy of the classifiers ranges from 62% to 65%, and their F1 measures from 67% to 71%. Classifiers achieved similar results on tweet data sets with and without emoticons, but slightly better results with the TF-IDF method of feature extraction.