Sentiment Analysis

This paper describes an information system for monitoring and analyzing reviews on social networks to form recommendations for the purchase of goods. This system is designed to be used by customers to speed up and facilitate the search for the necessary products on e-commerce resources. Successful selection of a quality product according to the desired criteria is extremely important, as it saves search time and customer money. Analyzing comments on the network, the information system recommends the product if there is a preponderance of positive feedback on it.

Machine learning algorithms have become very frequently used in natural language processing, notably sentiment analysis, which helps determine the general feeling carried within a text.  Among these algorithms, Support Vector Machines have proven powerful classifiers especially in such a task, when their performance is assessed through accuracy score and f1-score.  However, they remain slow in terms of training, thus making exhaustive grid-search experimentations very time-consuming.  In this paper, we present an observed pattern in SVM's accuracy, and f1-score approximated with a Lagrange

This paper describes the necessity of named-entity recognition for the implementation of sentiment analysis and presents methods and tools for recognition of appropriate named entities in Ukrainian restaurant reviews.

The paper deals with the means of expression of emotions in the language of the Internet. The existing methods of extraction of emotional elements in the text, as well as systems that implement the analysis of the emotional elements were analyzed in the article. Features of emotional elements are described, by means of which they are defined in the text. The use of the notation of Backus-Naur form to highlight the emotional elements was proposed in the paper. The proposed approach was implemented using the Java programming language.