# Stock Market Analysis with Text Data: A Review

arXiv: https://arxiv.org/abs/2106.12985 · HTML: https://mlsanity.com/paper/2106.12985/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 174 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://mlsanity.com/paper/1712.02136/) (2017)
- [Decision support from financial disclosures with deep neural networks and transfer learning](https://mlsanity.com/paper/1710.03954/) (2017)
- [Improving Stock Market Prediction via Heterogeneous Information Fusion](https://mlsanity.com/paper/1801.00588/) (2018)
- [Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks](https://mlsanity.com/paper/1506.07220/) (2015)
- [Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction](https://mlsanity.com/paper/1812.11226/) (2018)

## Built on by (most cited first, 1 of 1)

- [A three-step machine learning approach to predict market bubbles with financial news](https://mlsanity.com/paper/2510.16636/) (2025)

## Abstract (arXiv)

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.