Reducing the Degradation of Sentiment Analysis for Text Collections Spread over a Period of Time

This paper presents approaches to improve sentiment classification in dynamically updated text collections in natural language. As social networks are constantly updated by users there is essential to take into account new jargons, vital discussed topics while solving classification task. Therefore two fundamentally different methods for solution this problem are suggested. Supervised machine learning method and unsupervised machine learning method are used for sentiment analysis. The methods are compared and it is shown which method is most applicable in certain cases. Experiments comparing the methods on sufficiently representative text collections are described.