Numeral Understanding in Financial Tweets for Fine-Grained Crowd-Based Forecasting
Journal
2018 IEEE/WIC/ACM International Conference on Web Intelligence
Pages
136-143
ISBN
9781538673256
Date Issued
2019
Author(s)
Abstract
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. ? 2018 IEEE.
Subjects
Financial social media
Numeral corpus
Numeral understanding
SDGs
Description
18th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018, 3 December 2018 through 6 December 2018
Type
conference paper
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