maisМоделирование и анализ информационных системModeling and Analysis of Information Systems1818-10152313-5417Yaroslavl State University10.18255/1818-1015-2013-2-70-79mais-206Research ArticleОригинальные статьиArticlesПостроение модели для извлечения оценочной лексики в различных предметных областяхConstruction of a Model for the Cross-Domain Opinion Word ExtractionЛукашевичНаталья ВалентиновнаLoukachevitchN. V.

Научно-исследовательский вычислительный центр, ведущий научный сотрудник,

119991, Москва, ГСП-1, Ленинские горы, д. 1, стр. 4

 ведущий научный сотрудник, Research Computing Center,

Leninskiye Gory, 1, Build. 4, Moscow, GSP-1, 119991, Russia

louk@mail.cir.ruЧетвёркинИлья ИгоревичChetviorkinI. I.

Факультет вычислительной математики и кибернетики, аспирант,

119991, Москва, ГСП-1, Ленинские горы, д. 1, стр. 52

аспирант, Faculty of Computational Mathematics and Cybernetics,

Leninskiye Gory 1, Build. 52, Moscow, GSP-1, 119991, Russia

ilia2010@yandex.ruМосковский государственный университетРоссияLomonosov Moscow State UniversityRussian Federation2013200420132027079Copyright © Лукашевич Н.В., Четвёркин И.И., 20132013Лукашевич Н.В., Четвёркин И.И.Loukachevitch N.V., Chetviorkin I.I.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.mais-journal.ru/jour/article/view/206

В данной работе предлагается новый подход к извлечению оценочных слов для различных предметных областей. В рамках этого подхода была разработана модель, включающая набор характеристик и комбинацию алгоритмов, которые позволяют извлекать оценочные слова в конкретной предметной области. Данная модель была обучена в предметной области о фильмах и затем применена в четырёх других областях. Качество работы метода оценивалось на основании разметки экспертов и оставалось на высоком уровне при переносе модели на различные предметные области. Кроме того, созданная модель была использована в предметной области о фильмах на английском языке и продемонстрировала высокое качество извлечения оценочных слов.

In this paper we consider a new approach for domain-specific opinion word extraction in the Russian language. We propose a set of statistical features and an algorithm combination that can extract opinion words in a particular domain. The extraction model was trained in the movie domain and then applied to four other domains. The quality of the obtained sentiment lexicons was evaluated intrinsically on the base of an expert markup and remained on the high level during the model transfer to various domains. Finally, our method is adapted to the movie domain in English and it demonstrated good results.

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