PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media

Abstract

Automatic assessment of sentiment in large text corpora is an important goal in social sciences. This paper describes a methodology and the results of the development of a system for Russian language sentiment analysis that includes: a publicly available sentiment lexicon, a publicly available test collection with sentiment markup and a crowdsourcing website for such markup. The lexicon is aimed at detecting sentiment in user-generated content (blogs, social media) related to social and political issues. Its prototype was formed based on other dictionaries and on the topic modeling performed on a large collection of blog posts. Topic modeling revealed relevant (social and political) topics and as a result-relevant words for the lexicon prototype and relevant texts for the training collection. Each word was assessed by at least three volunteers in the context of three different texts where the word occurred while the texts received their sentiment scores from the same volunteers as well. Both texts and words were scored from −2 (negative) to +2 (positive). Of 7,546 candidate words, 2,753 got non-neutral sentiment scores. The quality of the lexicon was assessed with SentiStrength software by comparing human text scores with the scores obtained automatically based on the created lexicon. 93% of texts were classified correctly at the error level of ±1 class, which closely matches the result of SentiStrength initial application to the English language tweets. Negative classes were much larger and better predicted. The lexicon and the text collection are publicly available at http://linis-crowd.org.

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“…The corpus of restaurant reviews was expanded as part of the Russian track at the SemEval-2016 7 international competition [36]. Corpora of social media posts LinisCrowd 8 [37] and RuSentiment 9 [38], corpora of hotel reviews Russian Hotel Reviews 10 [39] and reviews about women's 3 http://romip.ru/ru/2011/index.html. 4 clothing and accessories RuReviews 11 [40] appeared later.…”

Semantic rule-based sentiment detection algorithm for Russian publicism sentences

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Analyzing COVID-related Stance and Arguments using BERT-based Natural Language Inference

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