Mining Sentiment Using Conversation Ontology

Mining Sentiment Using Conversation Ontology

Priti Srinivas Sajja (Sardar Patel University, India) and Rajendra Akerkar (Vestlandsforsking, Norway)
DOI: 10.4018/978-1-4666-2494-8.ch016
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Abstract

The research in the field of opinion mining has been ongoing for several years, and many models and techniques have been proposed. One of the techniques that can address the need for automated information monitoring to help to identify the trends and patterns that matter is sentiment mining. Existing approaches enable the analysis of a large number of text documents, mainly based on their statistical properties and possibly combined with numeric data. Most approaches are limited to simple word counts and largely ignore semantic and structural aspects of content. Conversation plays a vital role in expressing and promoting an opinion. In this chapter, the authors discuss the concept of ontology and propose a framework that allows the incorporation of information on conversation structure in the models for sentiment discovery in text.
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Ontology Fundamentals

In the broader context of the Semantic Web, applications need to understand by machine, which is being done with the help of the meaning associated with each component stored on the Web. Such capability of understanding is not covered by the traditional tools like mark up languages and protocols utilized on World Wide Web platform. There is a requirement of a component representation scheme called Ontology. Ontology interweaves human and computer understanding of symbols. These symbols, also known as terms, can be interpreted by both humans and machines. Ontology are means for conceptualizing and structuring knowledge. They are used for semantic annotation of resources in order to support information retrieval, automated inference, and interoperability among services and applications across the Web.

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