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Abstract. Natural language processing has recently become very popular in the sociological studies due to a wide expansion of social media such as social networks, blogs, forums, etc., as well as online polls. An important direction of this area is a text sentiment analysis, used to find out people's opinions on various actual issues. The paper deals with two methods of sentiment analysis: known support vector machine (SVM) as supervised learning and proposed lexicon-based classifier as unsupervised learning. The proposed classifier is domain-independent, does not require training data, and uses ready-made sentiment lexicons. The lexicon-based classifier is shown to exceed the SVM for small text collections. The article provides analysis of errors and offers the ways to increase classifier's quality.