Natural language processing (NLP), or the pragmatic research perspective of computational
linguistics, has become increasingly powerful due to data availability and various …

Natural language processing (NLP) is a part of the artificial intelligence domain focused on
communication between humans and computers. NLP attempts to address the inherent …

Personalized news recommendation is important for users to find interesting news
information and alleviate information overload. Although it has been extensively studied …

This paper proposes a novel application of deep learning models, Paragraph Vector, and
Long Short-Term Memory (LSTM), to financial time series forecasting. Investors make …

Our research examines a predictive machine learning approach for financial news articles
analysis using several different textual representations: bag of words, noun phrases, and …

With technological advancements that cultivate vibrant creation, sharing, and collaboration
among Web users, investors can rapidly obtain more valuable and timely information …

Can the choice of words and tone used by the authors of financial news articles correlate to
measurable stock price movements? If so, can the magnitude of price movement be …

We study the problem of correlating micro-blogging activity with stock-market events, defined
as changes in the price and traded volume of stocks. Specifically, we collect messages …

We address a text regression problem: given a piece of text, predict a real-world continuous
quantity associated with the text's meaning. In this work, the text is an SEC-mandated …

In this paper, we describe NewsCATS (news categorization and trading system), a system
implemented to predict stock price trends for the time immediately after the publication of …