Abstract:

This paper describes the Information extraction system that was presented at SentiRuEval-2015: aspect-based sentiment analysis of users' reviews in Russian. The proposed system uses a conditional random field algorithm to extract aspect terms mentioned in the text. A set of morphological features was used for machine learning. The system intent to perform two subtasks, Task A – automatic extraction of explicit aspects and Task B – automatic extraction of all aspects (explicit, implicit and sentiment facts), and tested on two domains: restaurants and automobiles. Our systems performed competitively and showed the results comparable to those of the other 10 participants.

Abstract:

The article investigates the problem of aspect-based sentiment analysis. Such version of analysis is more challenging compared to general task of sentiment detection problem. It implies the solutions to the number of related subtasks such as aspect term extraction, aspect term polarity detection and aspect category polarity detection. The solution of aspect-based sentiment analysis problem significantly extends the capabilities of natural language processing systems.

The article gives the overview of previous works in the field and describes the train and test data from the Russian evaluation workshop SentiRuEval. For the task of aspect term extraction the vector space of distributed representations of words was used. Aspect term detection is based on mutual information method and semantic similarity. The paper contains the number of experimental results. At the end the final conclusions are drawn.