A surge in public opinions mining against various societal topics using publicly available off-the-shelf sentiment analysis tools is evident in recent times. Since sentiment analysis is a domain-dependent problem, and the majority of the tools are built for customer reviews, the suitability of using such existing off-the-the-shelf tools for a societal topic is subject to investigation. None of the existing studies has thoroughly investigated on societal issues. This paper systematically evaluates the performance of 10 popularly used off-the-shelf tools and 17 state-of-the-art machine learning techniques and investigates their strengths and weaknesses using various societal and non-societal topics.

中文翻译：

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关于社会话题的情感分析工具和技术的实证研究

最近，使用公开可用的现成情绪分析工具针对各种社会话题进行民意挖掘的激增是显而易见的。由于情感分析是一个依赖领域的问题，并且大多数工具都是为客户评论而构建的，因此将此类现有的现成工具用于社会主题的适用性有待调查。现有的研究都没有对社会问题进行彻底调查。本文系统地评估了 10 种常用的现成工具和 17 种最先进的机器学习技术的性能，并使用各种社会和非社会主题调查了它们的优缺点。