Sentiment analysis is one of the most challenging tasks in natural language processing
(NLP). The extensively used application of sentiment analysis is sentiment classification of …

With the fast popularization and continued development of web pages on the Internet, text
classification has become a very serious problem in organizing and managing large …

Purpose Predicting the stock market's prices has always been an interesting topic since its
closely related to making money. Recently, the advances in natural language processing …

High volatility presents considerable challenges in the optimization of financial portfolio
assets. This study develops and explores model-based reinforcement learning (MBRL) in …

Abstract Recurrent Neural Networks are classes of Artificial Neural Networks that establish
connections between various nodes in a directed or undirected graph for investigation of the …

A significant number of individuals are facing mental health issues due to a lack of timely
treatment and support for detecting depression. This lack of early treatment is a primary …

Stock index fluctuations are characterized by high noise and their accurate prediction is
extremely challenging. To address this challenge, this study proposes a spatial–temporal …

Stock investment is rapidly increasing in Indonesia, focusing significantly on
telecommunication stocks. As the market grows, it becomes more critical for investors to …

From massive and complex spatial database, the useful information and knowledge are
extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm …

The rapid development of Internet finance has impacted traditional investment patterns, and
Internet money funds (IMFs) are involved extensively in finance. This research constructed a …