fastText-based methods for Emotion Identification in Russian Internet Discourse

Babii, Alexander; Kazyulina, Marina; Malafeev, Alexey

doi:10.1145/3447535.3462499

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the significant Russian-speaking population and the ever-growing level of Internet penetration, texts published by Russian-speaking users on social networks are attracting more and more attention from researchers. As a result, every year new works appear both in the classical analysis of the sentiment of Russian-language content ( e.g ., Araslanov, Komotskiy & Agbozo, 2020 ; Kanev et al, 2022 ; Kausar, Soosaimanickam & Nasar, 2021 ) and in related areas, such as the identification of emotions ( e.g ., Babii, Kazyulina & Malafeev, 2020 ; Kazyulina, Babii & Malafeev, 2020 ; Babii, Kazyulina & Malafeev, 2021 ), toxicity and hate speech detection ( e.g ., Zueva, Kabirova & Kalaidin, 2020 ; Pronoza et al, 2021 ; Smetanin & Komarov, 2021b ), and inappropriate language identification ( e.g ., Babakov et al, 2021 ; Babakov, Logacheva & Panchenko, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian

Smetanin¹

2022

PeerJ Computer Science

View full text Add to dashboard Cite

The Russian language is still not as well-resourced as English, especially in the field of sentiment analysis of Twitter content. Though several sentiment analysis datasets of tweets in Russia exist, they all are either automatically annotated or manually annotated by one annotator. Thus, there is no inter-annotator agreement, or annotation may be focused on a specific domain. In this article, we present RuSentiTweet, a new sentiment analysis dataset of general domain tweets in Russian. RuSentiTweet is currently the largest in its class for Russian, with 13,392 tweets manually annotated with moderate inter-rater agreement into five classes: Positive, Neutral, Negative, Speech Act, and Skip. As a source of data, we used Twitter Stream Grab, a historical collection of tweets obtained from the general Twitter API stream, which provides a 1% sample of the public tweets. Additionally, we released a RuBERT-based sentiment classification model that achieved F1 = 0.6594 on the test subset.

show abstract

Section: Related Workmentioning

confidence: 99%

RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian

Smetanin¹

2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

fastText-based methods for Emotion Identification in Russian Internet Discourse

Cited by 1 publication

References 16 publications

RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian

RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian

Contact Info

Product

Resources

About