@article{cambria2013semantic,
author = {Cambria, Erik and Song, Yangqiu and Wang, Haixun and Howard, Newton},
title = {Semantic multi-dimensional scaling for open-domain sentiment analysis},
year = {2013},
month = {January},
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 common-sense knowledge humans normally acquire during the formative years of their lives. In order to really understand natural language, a machine should be able to grasp such kind of knowledge, rather than merely relying on the valence of keywords and word co-occurrence frequencies. In this work, the largest existing taxonomy of common knowledge is blended with a natural-language-based semantic network of common-sense knowledge, and multi-dimensional scaling is applied on the resulting knowledge base for open-domain opinion mining and sentiment analysis.},
url = {http://approjects.co.za/?big=en-us/research/publication/semantic-multi-dimensional-scaling-for-open-domain-sentiment-analysis/},
journal = {IEEE Intelligent Systems},
}