CLaC and CLaC-NB
Alina Andreevskaia, Sabine Bergler · 2007
For the Affective Text task at Semeval-1/Senseval-4, the CLaC team compared a knowledge-based, domain-independent approach and a standard, statistical machine learning approach to ternary sentiment annotation of news headlines. In this paper we describe the two systems submitted to the competition and evaluate their results. We show that the knowledge-based unsupervised method achieves high accuracy and precision but low recall, while supervised statistical approach trained on small amount of in-domain data provides relatively high recall at the cost of low precision.