Lexicon Creation for Financial Sentiment Analysis Using Network Embedding

In this study, we aim to construct a polarity dictionary specialized for the analysis of financial policies. Based on an idea that polarity words are likely located in the secondary proximity in the dependency network, we proposed an automatic dictionary construction method using secondary LINE (Large-scale Information Network Embedding) that is a network representation learning method to quantify relationship. The results suggested the possibility of constructing a dictionary using distributed representation by LINE. We also confirmed that a distributed representation with a property different from the distributed representation by the CBOW (Continuous Bag of Word) model was acquired and analyzed the differences between the distributed representation using LINE and the distributed representation using the CBOW model.

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