ellibsЭлектронные библиотекиRussian Digital Libraries Journal1562-5419Казанский (Приволжский) федеральный университетellibs-433Research ArticleСтатьиТестирование методов анализа тональности текста, основанных на словаряхSentiment classification of reviews and twitter posts based on dictionariesТутубалинаЕ. В.elvtutubalina@kpfu.ruИвановВ. В.vivanov@kpfu.ruЗагуловаМ.lolmariya@gmail.comМингазовН.icrotek547@gmail.comАлимоваИ.alimovaIlseyar@gmail.comМалыхВ.alem.mipt@gmail.comВысшая школа Информационных технологий и информационных систем Казанского федерального университетаRussian FederationИнститут системного анализа РАНRussian Federation201528062015183-4138162Copyright © Тутубалина Е.В., Иванов В.В., Загулова М., Мингазов Н., Алимова И., Малых В., 20152015Тутубалина Е.В., Иванов В.В., Загулова М., Мингазов Н., Алимова И., Малых В.Тутубалина Е.В., Иванов В.В., Загулова М., Мингазов Н., Алимова И., Малых В.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/433

Технологии анализа тональности текста развиваются интенсивно, что обусловлено ростом объемов открытых источников, представляющих мнения пользователей интернета по различным вопросам. В статье описаны методы для анализа тональности текстов отзывов и коротких сообщений (твитов), приводятся результаты оценки их качества, которая производилась в рамках российского семинара SentiRuEval-2015.

Sentiment analysis and opinion mining technologies are growing fast. This is mostly due to a rapid grow of the data sources consisting a vast amount of user opinions and reviews on a wide set of topics. In this paper we describe methods for sentiment analysis of reviews and short messages (tweets), as well as evaluation of results obtained during SentiRuEval-2015.

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