  Stock market movements are influenced by public and private information
shared through news articles, company reports, and social media discussions.
Analyzing these vast sources of data can give market participants an edge to
make profit. However, the majority of the studies in the literature are based
on traditional approaches that come short in analyzing unstructured, vast
textual data. In this study, we provide a review on the immense amount of
existing literature of text-based stock market analysis. We present input data
types and cover main textual data sources and variations. Feature
representation techniques are then presented. Then, we cover the analysis
techniques and create a taxonomy of the main stock market forecast models.
Importantly, we discuss representative work in each category of the taxonomy,
analyzing their respective contributions. Finally, this paper shows the
findings on unaddressed open problems and gives suggestions for future work.
The aim of this study is to survey the main stock market analysis models, text
representation techniques for financial market prediction, shortcomings of
existing techniques, and propose promising directions for future research.
