Prior Polarity Dictionary Derived from SentiWordNet based on Random Forest Algorithm
Authors
Xiaobin Li, Yongquan Dong, Gai-Ge Wang, Mo Hou
Corresponding Author
Xiaobin Li
Available Online March 2017.
- DOI
- 10.2991/amcce-17.2017.145How to use a DOI?
- Keywords
- Sentiment Analysis; Sentiment Strength; Support Vector Regression; Random Forest
- Abstract
The prior polarity of words, as a challenging problem, can make great contribution to the sentiment analysis task. In this paper, we propose a method to generate the prior polarity dictionary based on Random Forest (RF) learning algorithm. We compare the proposed approach with the previous methods. The experimental results show that it is better than the state-of-art Support Vector Regression (SVR) method and it can gain better performance.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiaobin Li AU - Yongquan Dong AU - Gai-Ge Wang AU - Mo Hou PY - 2017/03 DA - 2017/03 TI - Prior Polarity Dictionary Derived from SentiWordNet based on Random Forest Algorithm BT - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017) PB - Atlantis Press SP - 818 EP - 824 SN - 2352-5401 UR - https://doi.org/10.2991/amcce-17.2017.145 DO - 10.2991/amcce-17.2017.145 ID - Li2017/03 ER -