文献类型：期刊文献

英文题名：Sentiment Evaluation of Forex News

作者：Cheng, Zhou[1]; Qi, Tianmei[2]; Wang, Jixiang[2]; Zhou, Yu[3]; Wang, Zhihong[2]; Guo, Yi[2,3,4]; Zhao, Junfeng[1]

机构：[1] RandD Dept, III CFETS Information Technology [Shanghai] Co., Ltd, Shanghai, China; [2] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China; [3] Business Intelligence and Visualization Research Center, National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai, China; [4] School of Information Science and Technology, Shihezi University, Shihezi, China

年份：2019

起止页码：197

外文期刊名：ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings

收录：EI(收录号：20192206973809)

语种：英文

外文关键词：Semantics - Sentiment analysis - Learning algorithms

摘要：Sentiment analysis is significant for excavating text opinion. There are two issues in the foreign exchange (Forex) field. 1) In sentiment orientation, most researches focus on product reviews, lack finegrained sentiment analysis for Forex news. 2) In sentiment intensity, most works consider the intensity of sentiment words but ignore the significance of field characteristics. Aiming at the two problems, a fine-grained Sentiment Analysis model (shorted as WD-SA) is established, which integrates with the Weight of sentiment words and Domain features. First, the semantic information of text is embedded into a vector based on word2vec. Then, sentiment orientation is detected by a method, which combines machine learning algorithm and the weight of sentiment words. Finally, features are extracted to investigate the intensity of news. The experimental results show that our algorithm outperforms the stateof-the-art. ? 2019 Association for Computing Machinery.