The paper describes our approach to the task of sentiment analysis of tweets within SentiRuEval – an open evaluation of sentiment analysis systems for the Russian language. We took part in the task of sentiment analysis of Russian tweets concerning two types of organizations: banks and telecommunications companies. On both datasets, the participants were required to perform a three-way classification of tweets: positive, negative or neutral.

We used various statistical methods as basis for our machine learning algorithms. Linguistic features produced by our morpho-syntactic analyzer are applied to the classification. Syntactic relations proved to be a crucial feature for any statistical method evaluated, and SVM-based classification performed better than the others. Normalized words are another important feature for the algorithm.

The evaluation revealed that our method proved to be rather successful: we scored the first in three out of four evaluation measures.

In the modern world, the problems arising in the field of traffic are of great importance. In order to solve existing problems, various intelligent systems are being developed, one of which is the Smart City system. This work is devoted to the development of an information and analytical system (IAS) for controlling an intelligent traffic light. The presented system consists of two levels, each of which contains a set of specific operations. The first level is responsible for detecting objects, in particular pedestrians and cars at the intersection, and the second level calculates the operating time of traffic light signals for the control signal that is transmitted to the device. For comparative analysis, the combined method (HOG+SVM) Histogram of oriented gradients was chosen, based on counting the number of gradient directions on individual image areas and Support Vector Machines, which are used to construct hyperplanes in n-dimensional space in order to separate objects belonging to different classes. The results of an experimental study, during which the recognition of objects in images was carried out, showed the superiority of the developed information and analytical system over existing methods. The average accuracy of detecting pedestrians and cars through the IAS was 69.4%. In addition, according to the experiment, it was concluded that the accuracy of detecting objects in images is directly proportional to the distance from the video camera to the object.