Text mining in big data analytics is emerging as a powerful tool for harnessing the power of
unstructured textual data by analyzing it to extract new knowledge and to identify significant …

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

Predicting stock market movements is a well-known problem of interest. Now-a-days social
media is perfectly representing the public sentiment and opinion about current events …

Financial markets play an important role on the economical and social organization of
modern society. In these kinds of markets, information is an invaluable asset. However, with …

The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …

Social Networks' omnipresence and ease of use has revolutionized the generation and
distribution of information in today's world. However, easy access to information does not …

This work uses deep learning methods for intraday directional movements prediction of
Standard & Poor's 500 index using financial news titles and a set of technical indicators as …

Accurate time-series forecasting is vital for numerous areas of application such as
transportation, energy, finance, economics, etc. However, while modern techniques are able …

The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …

Traditional stock market prediction approaches commonly utilize the historical price-related
data of the stocks to forecast their future trends. As the Web information grows, recently …