RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian

Anna Rogers; Alexey Romanov; Anna Rumshisky; Svitlana Volkova; Mikhail Gronas; Alex Gribov

RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian

Anna Rogers, Alexey Romanov, Anna Rumshisky, Svitlana Volkova, Mikhail Gronas, Alex Gribov

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

Abstract

This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.

Originalsprog	Engelsk
Titel	Proceedings of the 27th International Conference on Computational Linguistics
Antal sider	9
Udgivelsessted	Santa Fe, New Mexico, USA
Forlag	Association for Computational Linguistics
Publikationsdato	2018
Sider	755-763
Status	Udgivet - 2018

Emneord

RuSentiment dataset
sentiment analysis
social media
Russian language
annotation guidelines

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http://aclweb.org/anthology/C18-1064

Citationsformater

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title = "RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian",

abstract = "This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss{\textquoteright} kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.",

keywords = "RuSentiment dataset, sentiment analysis, social media, Russian language, annotation guidelines, RuSentiment dataset, sentiment analysis, social media, Russian language, annotation guidelines",

author = "Anna Rogers and Alexey Romanov and Anna Rumshisky and Svitlana Volkova and Mikhail Gronas and Alex Gribov",

year = "2018",

language = "English",

pages = "755--763",

booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",

publisher = "Association for Computational Linguistics",

address = "United States",

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RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian. / Rogers, Anna; Romanov, Alexey; Rumshisky, Anna et al.
Proceedings of the 27th International Conference on Computational Linguistics. Santa Fe, New Mexico, USA: Association for Computational Linguistics, 2018. s. 755-763.

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

TY - GEN

T1 - RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian

AU - Rogers, Anna

AU - Romanov, Alexey

AU - Rumshisky, Anna

AU - Volkova, Svitlana

AU - Gronas, Mikhail

AU - Gribov, Alex

PY - 2018

Y1 - 2018

N2 - This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.

AB - This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.

KW - RuSentiment dataset

KW - sentiment analysis

KW - social media

KW - Russian language

KW - annotation guidelines

KW - RuSentiment dataset

KW - sentiment analysis

KW - social media

KW - Russian language

KW - annotation guidelines

M3 - Article in proceedings

SP - 755

EP - 763

BT - Proceedings of the 27th International Conference on Computational Linguistics

PB - Association for Computational Linguistics

CY - Santa Fe, New Mexico, USA

ER -

RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian

Abstract

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