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

TL;DR

This paper introduces a vector space model method to measure word sentiment orientation, using high-dimensional vectors and cosine distances, outperforming previous unsupervised techniques in accuracy and practicality.

Contribution

The paper presents a novel multi-dimensional vector space approach for sentiment measurement that improves accuracy and efficiency over existing unsupervised methods.

Findings

Outperforms previous unsupervised sentiment measurement methods

More practical and data-efficient approach

Demonstrates significant accuracy improvements

Abstract

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.