A New Approach for Measuring Sentiment Orientation based on Multi-Dimensional Vector Space

This study implements a vector space model approach to measure the sentiment orientations of words. Two representative vectors for positive/negative polarity are constructed using high-dimensional vector space in both an unsupervised and a semisupervised manner. A sentiment orientation value per word is determined by taking the difference between the cosine distances against the two reference vectors. These two conditions (unsupervised and semi-supervised) are compared against an existing unsupervised method (Turney, 2002). As a result of our experiment, we demonstrate that this novel approach significantly outperforms the previous unsupervised approach and is more practical and data effi-