Sentiment evaluation of forex news

Abstract

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 fine-grained 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 state-of-the-art.