Although deep neural networks are excellent for text sentiment analysis, their applications in
real-world practice are occasionally limited owing to their black-box property. In this study …

For reducing investment risks, predicting the volatility of financial markets is crucial. We
propose a method for effectively embedding social media posts to facilitate accurate …

This paper proposes and analyzes a methodology of forecasting movements of the analysts'
net income estimates and those of stock prices. We achieve this by applying natural …

Word-level contextual sentiment analysis (WCSA) is an important task for mining reviews or
opinions. When analyzing this type of sentiment in the industry, both the interpretability and …

This study aims to visualize financial documents in such a way that even nonexperts can
understand the sentiments contained therein. To achieve this, we propose a novel text …

This article proposes a methodology to forecast the movements of analysts' estimated net
income and stock prices using analyst profiles. Our methodology is based on applying …

This study demonstrates whether analysts' sentiment toward individual stocks is useful in
predicting the macroeconomic index. This can be achieved by using natural language …

This study aims to evaluate the sentiment of financial texts using large language
models~(LLMs) and to empirically determine whether LLMs exhibit company-specific biases …

Although deep neural networks are excellent for text sentiment analysis, their applications in
real-world practice are occasionally limited owing to their black-box property. In response …

Nowadays, crypto-asset is gaining immense interest in the field of finance. Bitcoin is a one
such crypto-asset with a trading volume of more than 5 billion a day. On social networking …