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Syntax-based Sentiment analysis of tweet in Russian. / Adaskina, Yu. V.; Panicheva, P.V.; Popov, A.M.

Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (Москва, 27–30 мая 2015 г.). Вып. 14 (21): В 2 т. Т. 2: Доклады специальных секций. М : Российский государственный гуманитарный университет, 2015. p. 1-11.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Harvard

Adaskina, YV, Panicheva, PV & Popov, AM 2015, Syntax-based Sentiment analysis of tweet in Russian. in Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (Москва, 27–30 мая 2015 г.). Вып. 14 (21): В 2 т. Т. 2: Доклады специальных секций. Российский государственный гуманитарный университет, М, pp. 1-11, Международная конференция "Диалог - 2015", Москва, Russian Federation, 27/05/15. <http://www.dialog-21.ru/digests/dialog2015/materials/pdf/AdaskinaYuVPanichevaPVPopovAM.pdf>

APA

Adaskina, Y. V., Panicheva, P. V., & Popov, A. M. (2015). Syntax-based Sentiment analysis of tweet in Russian. In Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (Москва, 27–30 мая 2015 г.). Вып. 14 (21): В 2 т. Т. 2: Доклады специальных секций (pp. 1-11). Российский государственный гуманитарный университет. http://www.dialog-21.ru/digests/dialog2015/materials/pdf/AdaskinaYuVPanichevaPVPopovAM.pdf

Vancouver

Adaskina YV, Panicheva PV, Popov AM. Syntax-based Sentiment analysis of tweet in Russian. In Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (Москва, 27–30 мая 2015 г.). Вып. 14 (21): В 2 т. Т. 2: Доклады специальных секций. М: Российский государственный гуманитарный университет. 2015. p. 1-11

Author

Adaskina, Yu. V. ; Panicheva, P.V. ; Popov, A.M. / Syntax-based Sentiment analysis of tweet in Russian. Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (Москва, 27–30 мая 2015 г.). Вып. 14 (21): В 2 т. Т. 2: Доклады специальных секций. М : Российский государственный гуманитарный университет, 2015. pp. 1-11

BibTeX

@inproceedings{7e61140bd45f42ce83465d6f2536b203,
title = "Syntax-based Sentiment analysis of tweet in Russian",
abstract = "The paper describes our approach to the task of sentiment analysis of tweets within SentiRuEval—an open evaluation of sentiment analysis systems for the Russian language. We took part in the task of object-oriented sentiment analysis of Russian tweets concerning two types of organizations: banks and telecommunications companies. On both datasets, the participants were required to perform a three-way classification of tweets: positive, negative or neutral. We used various statistical methods as basis for our machine learning algorithms and checked which features would provide the best results. Syntactic relations proved to be a crucial feature to any statistical method evaluated, but SVM-based classification performed better than the others. Normalized words are another important feature for the algorithm. The evaluation revealed that our method proved to be rather successful: we scored the first in three out of four evaluation measures.",
keywords = "Sentiment analysis, syntactical relations, statistical methods, text classification, сентиментный анализ, синтаксические связи, статистические алгоритмы, классификация текстов",
author = "Adaskina, {Yu. V.} and P.V. Panicheva and A.M. Popov",
year = "2015",
language = "English",
pages = "1--11",
booktitle = "Компьютерная лингвистика и интеллектуальные технологии",
publisher = "Российский государственный гуманитарный университет",
address = "Russian Federation",
note = "null ; Conference date: 27-05-2015 Through 30-05-2015",

}

RIS

TY - GEN

T1 - Syntax-based Sentiment analysis of tweet in Russian

AU - Adaskina, Yu. V.

AU - Panicheva, P.V.

AU - Popov, A.M.

PY - 2015

Y1 - 2015

N2 - The paper describes our approach to the task of sentiment analysis of tweets within SentiRuEval—an open evaluation of sentiment analysis systems for the Russian language. We took part in the task of object-oriented sentiment analysis of Russian tweets concerning two types of organizations: banks and telecommunications companies. On both datasets, the participants were required to perform a three-way classification of tweets: positive, negative or neutral. We used various statistical methods as basis for our machine learning algorithms and checked which features would provide the best results. Syntactic relations proved to be a crucial feature to any statistical method evaluated, but SVM-based classification performed better than the others. Normalized words are another important feature for the algorithm. The evaluation revealed that our method proved to be rather successful: we scored the first in three out of four evaluation measures.

AB - The paper describes our approach to the task of sentiment analysis of tweets within SentiRuEval—an open evaluation of sentiment analysis systems for the Russian language. We took part in the task of object-oriented sentiment analysis of Russian tweets concerning two types of organizations: banks and telecommunications companies. On both datasets, the participants were required to perform a three-way classification of tweets: positive, negative or neutral. We used various statistical methods as basis for our machine learning algorithms and checked which features would provide the best results. Syntactic relations proved to be a crucial feature to any statistical method evaluated, but SVM-based classification performed better than the others. Normalized words are another important feature for the algorithm. The evaluation revealed that our method proved to be rather successful: we scored the first in three out of four evaluation measures.

KW - Sentiment analysis

KW - syntactical relations

KW - statistical methods

KW - text classification

KW - сентиментный анализ

KW - синтаксические связи

KW - статистические алгоритмы

KW - классификация текстов

UR - https://www.dialog-21.ru/media/2778/dialogue-2015_vol2.pdf

M3 - Conference contribution

SP - 1

EP - 11

BT - Компьютерная лингвистика и интеллектуальные технологии

PB - Российский государственный гуманитарный университет

CY - М

Y2 - 27 May 2015 through 30 May 2015

ER -

ID: 4787019