# Financial data analysis application via multi-strategy text processing

arXiv: https://arxiv.org/abs/2204.11394 · HTML: https://mlsanity.com/paper/2204.11394/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 87 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base](https://mlsanity.com/paper/2007.03875/) (2020)
- [OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction](https://mlsanity.com/paper/1909.13078/) (2019)
- [Prompt-Learning for Fine-Grained Entity Typing](https://mlsanity.com/paper/2108.10604/) (2021)
- [Neural Entity Linking: A Survey of Models Based on Deep Learning](https://mlsanity.com/paper/2006.00575/) (2020)
- [Fine-Grained Entity Typing for Domain Independent Entity Linking](https://mlsanity.com/paper/1909.05780/) (2019)
- [Network-based Fake News Detection: A Pattern-driven Approach](https://mlsanity.com/paper/1906.04210/) (2019)
- [Neural Machine Translating from Natural Language to SPARQL](https://mlsanity.com/paper/1906.09302/) (2019)
- [A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing](https://mlsanity.com/paper/1912.07700/) (2019)
- [Fine-grained Entity Typing via Label Reasoning](https://mlsanity.com/paper/2109.05744/) (2021)
- [Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey](https://mlsanity.com/paper/2002.06612/) (2020)
- [Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking](https://mlsanity.com/paper/1807.05127/) (2018)
- [MetaAID: A Flexible Framework for Developing Metaverse Applications via AI Technology and Human Editing](https://mlsanity.com/paper/2204.01614/) (2022)
- [Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention](https://mlsanity.com/paper/1803.04329/) (2018)

## Built on by (most cited first, 5 of 5)

- [Climate Change from Large Language Models](https://mlsanity.com/paper/2312.11985/) (2023)
- [Industry Risk Assessment via Hierarchical Financial Data Using Stock Market Sentiment Indicators](https://mlsanity.com/paper/2303.02707/) (2023)
- [MetaAID 2.5: A Secure Framework for Developing Metaverse Applications via Large Language Models](https://mlsanity.com/paper/2312.14480/) (2023)
- [Extracting Relational Triples Based on Graph Recursive Neural Network via Dynamic Feedback Forest Algorithm](https://mlsanity.com/paper/2308.11411/) (2023)
- [Reranking Passages with Coarse-to-Fine Neural Retriever Enhanced by List-Context Information](https://mlsanity.com/paper/2308.12022/) (2023)

## Abstract (arXiv)

Maintaining financial system stability is critical to economic development, and early identification of risks and opportunities is essential. The financial industry contains a wide variety of data, such as financial statements, customer information, stock trading data, news, etc. Massive heterogeneous data calls for intelligent algorithms for machines to process and understand. This paper mainly focuses on the stock trading data and news about China A-share companies. We present a financial data analysis application, Financial Quotient Porter, designed to combine textual and numerical data by using a multi-strategy data mining approach. Additionally, we present our efforts and plans in deep learning financial text processing application scenarios using natural language processing (NLP) and knowledge graph (KG) technologies. Based on KG technology, risks and opportunities can be identified from heterogeneous data. NLP technology can be used to extract entities, relations, and events from unstructured text, and analyze market sentiment. Experimental results show market sentiments towards a company and an industry, as well as news-level associations between companies.