Abstract
Sentiment lexicons with valence-Arousal ratings are useful resources for the development of dimensional sentiment applications. In order to solve the significant lack of Chinese valence and arousal lexicons, the objective of the DSAW is to automatically acquire the valence-Arousal ratings of Chinese affective words. In this task, we develop a novel approach that integrate word embeddings into a graph-based model with K-Nearest Neighbor to identify both valence and arousal dimensions. We also propose to use character embeddings to represent unseen words, which is a major challenge in collecting large corpora. The evaluation results demonstrate that our system is effective in dimensional sentiment analysis for Chinese words with 0.847 and 1.281 mean absolute error (MAE) for valence and arousal respectively.
Original language | English |
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Title of host publication | Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 297-299 |
Number of pages | 3 |
ISBN (Electronic) | 9781509009213 |
DOIs | |
Publication status | Published - Mar 10 2017 |
Externally published | Yes |
Event | 20th International Conference on Asian Language Processing, IALP 2016 - Tainan, Taiwan Duration: Nov 21 2016 → Nov 23 2016 |
Conference
Conference | 20th International Conference on Asian Language Processing, IALP 2016 |
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Country/Territory | Taiwan |
City | Tainan |
Period | 11/21/16 → 11/23/16 |
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Linguistics and Language
- Artificial Intelligence