Using DistribUteD representations for aspect-baseD sentiment analysis

The article is focused on aspect-based sentiment analysis, which is a specific version of the general sentiment analysis task. Its goal is to detect the opinions expressed in the text on the level of significant aspects of the specified entity. An overview of the existing approaches and previous work is presented. The main result of our work is a new method of aspect-based sentiment analysis based on the distributed representations of words. Such representations are obtained by using deep learning algorithms. The method includes the well-known algorithm of training distributed representations of words, two new techniques for constructing the aspect and sentiment lexicons, and an algorithm for calculating aspect scores. Examples of aspect and sentiment terms are given. The vectors of resulting terms are visualized using the t-SNE method. The article presents the results of experiments on a test corpus for three aspects—“food”, “interior” and “service”, which yield aF1-measure increase of 11 to 16% as compared to the baseline.