  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 vec-tor space in both an
unsupervised and a semi-supervised manner. A sentiment ori-entation value per
word is determined by taking the difference between the cosine distances
against the two reference vec-tors. These two conditions (unsupervised and
semi-supervised) are compared against an existing unsupervised method (Turney,
2002). As a result of our experi-ment, we demonstrate that this novel ap-proach
significantly outperforms the pre-vious unsupervised approach and is more
practical and data efficient as well.
