Emotion ontologies have been developed to capture affect, a concept that encompasses discrete emotions and feelings, especially for research on sentiment analysis, which analyzes a customer's attitude towards a company or a product. However, there have been limited efforts to adapt and employ these ontologies. This research surveys and synthesizes emotion ontology studies to develop a Framework of Emotion Ontologies that can be used to help a user select or design an appropriate emotion ontology to support sentiment analysis and increase the user's understanding of the roles of affect, context, and behavioral information with respect to sentiment. The framework, which is derived from research on emotion ontologies, psychology, and sentiment analysis, classifies emotion ontologies as discrete emotion or one of two hybrid ontologies that are combinations of the discrete, dimensional, or componential process emotion paradigms. To illustrate its usefulness, the framework is applied to the development of an emotion ontology for a sentiment analysis application.

The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.

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Introduction

论文的引言部分介绍了意见挖掘（或称情感分析）的重要性，指出该领域受到广泛关注的原因是其广泛的应用前景和研究挑战。引言中讨论了两大关键问题：观点词汇扩展和观点目标提取。

• 观点词汇扩展：指的是在不同领域中，情感表达可能会用到不同的词汇，因此一个通用的情感词典很难满足所有领域的需求。为了提升情感分析的效果，有必要根据特定领域文本扩展已有的情感词汇表。

• 观点目标提取：即提取出文本中情感表达所指向的对象（如“电池寿命”），以便理解情感指向，增加情感分析的实际价值。

作者提出了一种基于依赖关系的双重传播方法，通过句法关系在观点词和目标词之间传播信息，逐步扩展词汇和提取目标。这种方法具有半监督的特点，因为它只需要一个初始的观点词词典来启动传播过程。实验表明，与其他现有方法相比，该方法在扩展词汇和提取目标方面均表现出更高的准确性。

Related Work

论文的相关工作部分总结了在观点词提取和目标提取方面的研究进展。

1. 观点词提取：

• 语料库驱动方法：利用词的分布相似性和统计共现（如Turney和Littman的方法）来提取观点词。这类方法依赖于较大规模的语料，但在小规模数据集上表现不佳。