Maximum Entropy Model based on Feature Extraction for Sentiment Detection of Text
- DOI
- 10.2991/wartia-16.2016.272How to use a DOI?
- Keywords
- Sentiment Detection, Feature Extraction, Maximum Entropy Model
- Abstract
The rapid development of social media services has facilitated the communication of opinions through online news, blogs, post bar, microblogs/tweets, and so forth. This article concentrates on the mining of emotions evoked by newmaterials. Compared to the classical sentiment analysis by using the word-emotion lexicon in the text, we combine the word with emotion via the intensive feature functions. We propose a maximum entropy model based on the feature extraction for sentiment classification, which generates the probability of sentiments conditioned to news text. In addition, one effective feature extraction strategies are proposed to refine the original miscellaneous news text. Experimental evaluations using real-world data validate the effectiveness of the proposed model on sentiment classification of news text.
- Copyright
- © 2016, 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 - Jun Li AU - Wei Jin AU - Zihao Zhang PY - 2016/05 DA - 2016/05 TI - Maximum Entropy Model based on Feature Extraction for Sentiment Detection of Text BT - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications PB - Atlantis Press SP - 1298 EP - 1305 SN - 2352-5401 UR - https://doi.org/10.2991/wartia-16.2016.272 DO - 10.2991/wartia-16.2016.272 ID - Li2016/05 ER -