Sentiment analysis becomes increasingly popular with the rapid growth of various reviews, survey responses, tweets or posts available from social media like Facebook or Twitter. Sentiment analysis can be turned into the question of whether a piece of text is expressing positive, negative or neutral sentiment towards the discussed topic and can be thus understood as a knowledge-based classification problem. A variety of knowledge-based techniques can be used to solve this problem. The paper focuses on two complementary approaches that originate in the area of AI (artificial intelligence), rule-based reasoning and case-based reasoning. We describe basic principles of both approaches, their strengths and limitations and, based on a review of literature, show how these approaches can be used for sentiment analysis.

中文翻译：

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使用基于规则和基于案例的推理进行情感分析

随着 Facebook 或 Twitter 等社交媒体上的各种评论、调查回复、推文或帖子的快速增长，情绪分析变得越来越流行。情感分析可以转化为一段文本是否对所讨论的主题表达积极、消极或中性情感的问题，因此可以理解为基于知识的分类问题。可以使用多种基于知识的技术来解决这个问题。本文重点介绍源自 AI（人工智能）领域的两种互补方法，即基于规则的推理和基于案例的推理。我们描述了这两种方法的基本原理、它们的优势和局限性，并根据文献综述展示了这些方法如何用于情感分析。