A New Approach for Measuring Sentiment Orientation based on Multi-Dimensional Vector Space
This addresses sentiment analysis for natural language processing, but it is incremental as it builds on prior vector-based methods.
The study tackled measuring word sentiment orientation by implementing a vector space model with positive/negative reference vectors in unsupervised and semi-supervised ways, and it significantly outperformed an existing unsupervised method, showing improved performance and data efficiency.
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.