Abstract

The article is devoted to the task of sentiment detection of Russian sentences. The sentiment is conceived as the author’s attitude to the topic of a sentence. This assay considers positive, neutral, and negative sentiment classes, i.e., the task of three-classes classification is solved. The article introduces a rule-based sentiment detection algorithm for Russian sentences. The algorithm is based on the assumption that the sentiment of a phrase can be determined by the sentiments of its parts by the recursive application of appropriate semantic rules to the sentiments of its parts organized as a constituency parse tree. The utilized set of semantic rules was constructed based on a discussion with experts in linguistics. The experiments showed that the proposed recursive algorithm performs slightly worse on the hotel reviews corpus than the adapted rule-based approach: weighted F1-measures are 0.75 and 0.78, respectively. To measure the algorithm efficiency on complex sentences, we created OpenSentimentCorpus based on OpenCorpora, an open corpus of sentences extracted from Russian news and periodicals. On OpenSentimentCorpus the recursive algorithm performs be.er than the adapted approach does: F1-measures are 0.70 and 0.63, respectively. This indicates that the proposed algorithm has an advantage in case of more complex sentences with more subtle ways of expressing the sentiment.

中文翻译：

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俄语句子的递归情感检测算法

摘要

这篇文章致力于俄语句子的情感检测任务。情感被认为是作者对句子主题的态度。该分析考虑了积极、中性和消极情绪类别，即解决了三类分类的任务。本文介绍了一种基于规则的俄语句子情感检测算法。该算法基于这样的假设：短语的情感可以通过将适当的语义规则递归应用到组织为选区解析树的其部分的情感来由其各部分的情感来确定。所使用的语义规则集是根据与语言学专家的讨论构建的。实验表明，所提出的递归算法在酒店评论语料库上的表现比采用的基于规则的方法稍差：加权 F1 测量值分别为 0.75 和 0.78。为了衡量复杂句子的算法效率，我们基于 OpenCorpora 创建了 OpenSentimentCorpus，这是一个从俄罗斯新闻和期刊中提取的开放句子语料库。在 OpenSentimentCorpus 上，递归算法的性能优于调整后的方法：F1 测量值分别为 0.70 和 0.63。这表明所提出的算法在更复杂的句子和更微妙的情感表达方式的情况下具有优势。