Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Sentiment Analysis in the Bio-Medical DomainSenticNet

Sentiment Analysis in the Bio-Medical Domain: SenticNet [SenticNet is the knowledge base the sentic computing framework leverages on for concept-level sentiment analysis. This chapter illustrates how such a resource is built. In particular, the chapter thoroughly explains the processes of knowledge acquisition, representation, and reasoning, which contribute to the generation of the semantics and sentics that form SenticNet. This chapter describes the knowledge bases and knowledge sources SenticNet is built upon. Then it describes how the knowledge collected is represented in graph, matrix and vector space. Then it dives into the techniques adopted for generating semantics and sentics, finally discussing how the proposed framework outperforms the state-of-the-art methods.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Sentiment Analysis in the Bio-Medical DomainSenticNet

Download PDF
64 pages

Loading next page...
 
/lp/springer-journals/sentiment-analysis-in-the-bio-medical-domain-senticnet-IjEYfcgI4I

References (0)

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG 2017
ISBN
978-3-319-68467-3
Pages
39–103
DOI
10.1007/978-3-319-68468-0_3
Publisher site
See Chapter on Publisher Site

Abstract

[SenticNet is the knowledge base the sentic computing framework leverages on for concept-level sentiment analysis. This chapter illustrates how such a resource is built. In particular, the chapter thoroughly explains the processes of knowledge acquisition, representation, and reasoning, which contribute to the generation of the semantics and sentics that form SenticNet. This chapter describes the knowledge bases and knowledge sources SenticNet is built upon. Then it describes how the knowledge collected is represented in graph, matrix and vector space. Then it dives into the techniques adopted for generating semantics and sentics, finally discussing how the proposed framework outperforms the state-of-the-art methods.]

Published: Jan 24, 2018

Keywords: Knowledge representation and reasoning; Semantic network; Vector space model; Spreading activation; Emotion categorization; Sentic computing

There are no references for this article.