Isanette: A common and common sense knowledge base for opinion mining

Cambria, Erik; Song, Yangqiu; Wang, Haixun; Hussain, Amir

Abstract

The ability to understand natural language text is far from being emulated in machines. One of the main hurdles to overcome is that computers lack both the common and the common sense knowledge humans normally acquire during the formative years of their lives. If we want machines to really understand natural language, we need to provide them with this kind of knowledge rather than relying on the valence of keywords and word co-occurrence frequencies. In this work, we blend the largest existing taxonomy of common knowledge with a natural-language-based semantic network of common sense knowledge, and use multi-dimensionality reduction techniques on the resulting knowledge base for opinion mining and sentiment analysis.

Citation

Cambria, E., Song, Y., Wang, H., & Hussain, A. (2011, December). Isanette: A common and common sense knowledge base for opinion mining. Presented at 2011 IEEE 11th International Conference on Data Mining Workshops, Vancouver, BC, Canada

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2011 IEEE 11th International Conference on Data Mining Workshops
• Start Date: Dec 11, 2011
• End Date: Dec 11, 2011
• Online Publication Date: Jan 23, 2012
• Publication Date: 2011
• Deposit Date: Oct 15, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 315-322
• Series Title: IEEE International Conference on Data Mining workshops
• Series ISSN: 2375-9232
• Book Title: 2011 IEEE 11th International Conference on Data Mining Workshops
• ISBN: 978-1-4673-0005-6
• DOI: https://doi.org/10.1109/ICDMW.2011.106
• Public URL: http://researchrepository.napier.ac.uk/Output/1793357