Comparison among multilingual financial words using the word2vec and clustering with news resources for automatic creation of financial dictionaries

Abstract

Vector representation of words such as word2vec is an efficient method used in text mining. However, few papers are focusing on the multilingual studies. In this paper we present the comparative study on English and Japanese resources respectively, and then we try to investigate the possible relationship between the two vector models in two languages. We first extract two word2vec models by using news resources of ten years, and then we cluster them basing on their cosine similarity for both Japanese and English respectively. Second, we extract the words related to finance and then derive two dictionaries in two languages. Finally, we make a comparison between these two dictionaries and tempt to Sentiment estimation of a cluster of one language based on similar clusters of other language.