ellibsЭлектронные библиотекиRussian Digital Libraries Journal1562-5419Казанский (Приволжский) федеральный университетellibs-432Research ArticleСтатьиСемантическое сходство в задаче аспектно-эмоционального анализаSemantic similarity for aspect-based sentiment analysisКотельниковЕ. В.kotelnikov.ev@gmail.comБлиновП. Д.blinoff.pavel@gmail.comВятский государственный гуманитарный университетRussian Federation201528062015183-4120137Copyright © Котельников Е.В., Блинов П.Д., 20152015Котельников Е.В., Блинов П.Д.Котельников Е.В., Блинов П.Д.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ellibs.elpub.ru/jour/article/view/432

Исследуется проблема аспектно-эмоционального анализа текста. По сравнению с общим анализом тональности такой вариант является более сложным по причине наличия ряда сопутствующих подзадач, таких, как выделение аспектных терминов, определение тональности по отношению к этим терминам и аспектным категориям. Однако решение данной проблемы значительно расширяет возможности систем автоматического анализа неструктурированного текста. Приведен обзор предыдущих работ в области аспектно-эмоционального анализа, описаны обучающие и тестовые данные семинара SentiRuEval. Для задачи извлечения аспектных терминов использовано векторное пространство распределенных представлений слов. Тональность аспектных терминов определяется на основе функций совместной информации и семантического сходства. Приведены сравнительные результаты на тестовых данных и заключительные выводы.

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.

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The authors declare that there are no conflicts of interest present.