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Ontology-Based Model for Mining User’s Emotions on the Wisdom Web

  • Jing Chen
  • Bin HuEmail author
  • Philip Moore
  • Xiaowei Zhang
Chapter
Part of the Web Information Systems Engineering and Internet Technologies Book Series book series (WISE)

Abstract

The task of automatically detecting emotion on a web is challenging. This is due to the fact that a traditional web cannot directly interpret the meaning of semantic concepts or assess users emotions. We describe an ontology-based mining model for representation and integration of affect-related knowledge and apply it to detect user’s emotions. This application is a typical use case of the broad-based Wisdom Web of Things (W2T) methodology. The model (named BIO-EMOTION) acts as an integrated framework for: (1) representation and interpretation of affect-related knowledge, including user profile, bio-signal data, situation and environment factors, and (2) supporting intelligent reasoning on users’ emotions. To evaluate the effectiveness of the mining model, we conduct an experiment on a public dataset DEAP and capture a semantic knowledge base expressing both known and deduced knowledge. Evaluation shows that the model not only reaches higher accuracy than other emotion detection results from the same dataset but also achieves a comprehensive affect-related knowledge base which could represent things from both social world, physical world and cyber world in semantics. The ultimate goal of present research is to provide active, transparent, safe and reliable services to web users through their inner emotion. The model implements crucial sub-processes of W2T data cycle: from Things (acquisition of things in the hyper world) to Wisdom (performing intelligent reasoning on web users’ emotion). A long-term goal is to achieve the whole W2T data cycle and to realize a holistic intelligent mining model used on the Wisdom Web.

Keywords

Emotion Recognition Inference Rule Inference Engine Reasoning Rule Emotion Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the National Basic Research Program of China (973 Program) (No.2014CB744600), the National Natural Science Foundation of China (No.60973138 and No.61003240), the International Cooperation Project of Ministry of Science and Technology (No.2013DFA11140), the National Basic Research Program of China (973 Program) (No.2011CB711000), and Gansu Provincial Science & Technology Department (No. 1208RJYA015). The authors would like to acknowledge European Community’s Seventh Framework Program (FP7/2007–2011) for their public DEAP database.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jing Chen
    • 1
  • Bin Hu
    • 1
    Email author
  • Philip Moore
    • 1
  • Xiaowei Zhang
    • 1
  1. 1.School of Information Science and Engineering, Lanzhou UniversityLanzhouChina

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