This work presents and assesses the power of various volumetric, sentiment, and social
network approaches to predict crucial decisions from online social media platforms. The …

Abstract Social Media Prediction (SMP) is an emerging powerful tool attracting the attention
of researchers and practitioners alike. Despite its many merits, SMP has also several …

Crude oil plays an important role in the world economy and contributes to more than one
third of energy consumption worldwide. The better forecasting of its fluctuating price is …

Recent research has shown that introducing online data can significantly improve
forecasting ability. This study considers several popular single-model machine learning …

There is considerable interest in the impact of investor attention on investment returns,
especially for cryptocurrencies. However, previous research does not distinguish between …

This study proposes a novel data-driven crude oil price prediction methodology using
Google Trends and online media text mining. Convolutional neural network (CNN) is used to …

Stock price prediction has been a subject of significant interest in the financial mathematics
field. Recently, interest in natural language processing models has increased, and among …

Luminescence thermometry has substantially progressed in the last decade, rapidly
approaching the performance of concurrent technologies. Performance is usually assessed …

With the boom in big data, a promising idea for using search engine data has emerged and
improved international oil price prediction, a hot topic in the fields of energy system …

Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult
for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long …