Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
PolSentiLex : Sentiment Detection in Socio-Political Discussions on Russian Social Media. / Koltsova, Olessia; Alexeeva, Svetlana; Pashakhin, Sergei; Koltsov, Sergei.
Artificial Intelligence and Natural Language - 9th Conference, AINL 2020, Proceedings. ed. / Andrey Filchenkov; Janne Kauttonen; Lidia Pivovarova. Springer Nature, 2020. p. 1-16 (Communications in Computer and Information Science; Vol. 1292 CCIS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - PolSentiLex
T2 - 9th Conference on Artificial Intelligence and Natural Language, AINL 2020
AU - Koltsova, Olessia
AU - Alexeeva, Svetlana
AU - Pashakhin, Sergei
AU - Koltsov, Sergei
N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - We present a freely available Russian language sentiment lexicon PolSentiLex designed to detect sentiment in user-generated content related to social and political issues. The lexicon was generated from a database of posts and comments of the top 2,000 LiveJournal bloggers posted during one year (1.5 million posts and 20 million comments). Following a topic modeling approach, we extracted 85,898 documents that were used to retrieve domain-specific terms. This term list was then merged with several external sources. Together, they formed a lexicon (16,399 units) marked-up using a crowdsourcing strategy. A sample of Russian native speakers (n = 105) was asked to assess words’ sentiment given the context of their use (randomly paired) as well as the prevailing sentiment of the respective texts. In total, we received 59,208 complete annotations for both texts and words. Several versions of the marked-up lexicon were experimented with, and the final version was tested for quality against the only other freely available Russian language lexicon and against three machine learning algorithms. All experiments were run on two different collections. They have shown that, in terms of, lexicon-based approaches outperform machine learning by 11%, and our lexicon outperforms the alternative one by 11% on the first collection, and by 7% on the negative scale of the second collection while showing similar quality on the positive scale and being three times smaller. Our lexicon also outperforms or is similar to the best existing sentiment analysis results for other types of Russian-language texts.
AB - We present a freely available Russian language sentiment lexicon PolSentiLex designed to detect sentiment in user-generated content related to social and political issues. The lexicon was generated from a database of posts and comments of the top 2,000 LiveJournal bloggers posted during one year (1.5 million posts and 20 million comments). Following a topic modeling approach, we extracted 85,898 documents that were used to retrieve domain-specific terms. This term list was then merged with several external sources. Together, they formed a lexicon (16,399 units) marked-up using a crowdsourcing strategy. A sample of Russian native speakers (n = 105) was asked to assess words’ sentiment given the context of their use (randomly paired) as well as the prevailing sentiment of the respective texts. In total, we received 59,208 complete annotations for both texts and words. Several versions of the marked-up lexicon were experimented with, and the final version was tested for quality against the only other freely available Russian language lexicon and against three machine learning algorithms. All experiments were run on two different collections. They have shown that, in terms of, lexicon-based approaches outperform machine learning by 11%, and our lexicon outperforms the alternative one by 11% on the first collection, and by 7% on the negative scale of the second collection while showing similar quality on the positive scale and being three times smaller. Our lexicon also outperforms or is similar to the best existing sentiment analysis results for other types of Russian-language texts.
KW - Lexicon-based approach
KW - Russian language
KW - Sentiment analysis
KW - Social media
KW - Socio-political domain
UR - http://www.scopus.com/inward/record.url?scp=85092918682&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/2275a918-55a7-34d4-9edd-b028acaa3eda/
U2 - 10.1007/978-3-030-59082-6_1
DO - 10.1007/978-3-030-59082-6_1
M3 - Conference contribution
AN - SCOPUS:85092918682
SN - 9783030590819
T3 - Communications in Computer and Information Science
SP - 1
EP - 16
BT - Artificial Intelligence and Natural Language - 9th Conference, AINL 2020, Proceedings
A2 - Filchenkov, Andrey
A2 - Kauttonen, Janne
A2 - Pivovarova, Lidia
PB - Springer Nature
Y2 - 7 October 2020 through 9 October 2020
ER -
ID: 71870169