Name
Title | ||
|---|---|---|
A New Approach for Measuring Sentiment Orientation based on Multi-Dimensional Vector Space. |
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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computation and Language | Vector space,Multi dimensional,Trigonometric functions,Pattern recognition,Computer science,Artificial intelligence,Vector space model,Machine learning |
DocType | Volume | Citations |
Journal | abs/1801.00254 | 0 |
PageRank | References | Authors |
0.34 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youngsam Kim | 1 | 2 | 1.04 |
| Hyopil Shin | 2 | 53 | 10.09 |