SENTIMENT ANALYSIS AT THE LEVEL OF ASPECTS USING METHODS OF MACHINE LEARNING

摘要

FIELD: physics.;SUBSTANCE: they perform a syntactic-semantic analysis of a part of the text in the natural language for obtaining a set of syntactic-semantic structures. They perform an interpretation of syntactic-semantic structures using a set of production rules for detecting in the part of the text in the natural language an aspectual term representing the aspect associated with the target entity. The value of the classifier function is calculated, using the text characteristics obtained in the syntactic-semantic analysis, for determining the key note associated with the aspectual term. They create a report that contains a hierarchical list of aspectual terms that include the identified aspects and key tones of the identified aspects. The parameter of the classifier function is determined using a training data sample and a confirming data sample. The training data sample includes educational text in natural language containing a variety of aspectual terms.;EFFECT: improving the accuracy of sentiment analysis of texts in natural language.;14 cl, 21 dwg