# Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting

arXiv: https://arxiv.org/abs/1809.05356 · HTML: https://mlsanity.com/paper/1809.05356/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 6 of 6)

- [Representing Numbers in NLP: a Survey and a Vision](https://mlsanity.com/paper/2103.13136/) (2021)
- [Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance](https://mlsanity.com/paper/2502.18772/) (2025)
- [When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP](https://mlsanity.com/paper/2305.14007/) (2023)
- [How to Leverage Digit Embeddings to Represent Numbers?](https://mlsanity.com/paper/2407.00894/) (2024)
- [Wiki-Quantities and Wiki-Measurements: Datasets of Quantities and their Measurement Context from Wikipedia](https://mlsanity.com/paper/2503.14090/) (2025)
- [JFinTEB: Japanese Financial Text Embedding Benchmark](https://mlsanity.com/paper/2604.15882/) (2026)

## Abstract (arXiv)

Numerals that contain much information in financial documents are crucial for financial decision making. They play different roles in financial analysis processes. This paper is aimed at understanding the meanings of numerals in financial tweets for fine-grained crowd-based forecasting. We propose a taxonomy that classifies the numerals in financial tweets into 7 categories, and further extend some of these categories into several subcategories. Neural network-based models with word and character-level encoders are proposed for 7-way classification and 17-way classification. We perform backtest to confirm the effectiveness of the numeric opinions made by the crowd. This work is the first attempt to understand numerals in financial social media data, and we provide the first comparison of fine-grained opinion of individual investors and analysts based on their forecast price. The numeral corpus used in our experiments, called FinNum 1.0 , is available for research purposes.