斯洛伐克语社交网络帖子的情感分析

Rastislav Krchnavy, Marián Simko
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引用次数: 13

摘要

在本文中,我们解决了在一个不太有针对性的语言-斯洛伐克的社交网络帖子的情感分析问题。小语种在这一领域的研究非常缺乏,因为它们经常为文本处理引入额外的语言特定问题。在斯洛伐克语中,常见的问题是高度反射,复杂的形态和句法。社交网络的用户生成内容引入了额外的挑战(变音符号的可变性、不一致的风格、高错误率),使任务变得更加困难。在本文中,我们提出了一种对Facebook社交网络帖子进行情感分析的方法。该方法基于机器学习,并结合了多层次文本预处理,旨在处理用户生成的社交内容的细节。在使用多家知名公司Facebook页面数据的真实世界环境中进行的评估表明,我们的方法的准确性可与世界主要语言的方法相媲美。
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Sentiment analysis of social network posts in Slovak language
In this paper we tackle the issue of sentiment analysis of social network posts in a not well targeted language — Slovak. There is a significant lack of research in this area for minor languages, as they often introduce additional language-specific issues for text processing. In case of Slovak, common issues are high flection, complex morphology and syntax. User-generated content of social networks introduces additional challenges (variability of diacritics, inconsistent style, high error rate) that make the task even harder. In this paper, we propose a method for sentiment analysis of social network posts on Facebook. The proposed method is based on machine learning and incorporates multilevel text pre-processing aiming to deal with specifics of user-generated social content. The evaluation in a real-word setting employing data from Facebook pages of multiple well-known companies shows accuracy of our method comparable with approaches for major world languages.
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