Sentiment Analysis with Parsed Representation of News Articles

Information published by financial news agencies is used as one of the inputs to make investment decisions. News articles from multiple sources can be used to gauge market sentiment towards an industry or a specific company. Deep learning techniques have been successful in producing state of the art results on various benchmark datasets (Dai & Le, 2015; Miyato et al., 2016). Most of the popular algorithms extract features from words, sentences or paragraphs and represent them as fixed-length vectors (Mikolov et al., 2013; Le & Mikolov, 2014). We propose the use of parsed representations of text along with fixed-length feature vectors as input for recurrent neural networks. The performance of these models will be evaluated on sentiment analysis tasks.

Intern: 
Nikhil Sapru
Faculty Supervisor: 
Graham Taylor
Province: 
Ontario
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