@inproceedings{9176e9f2121d49ac9b58b3d343e488d9,
title = "Automated investor sentiment classification using financial social media",
abstract = "As Fintech continuously disrupt the finance industry, big data from non-traditional sources have been increasingly used to extract investment signals with automated solutions. We leverage a large-scale user post dataset from a financial social media platform and to predict investor sentiment using the natural language processing techniques and machine learning models. The BERT model built and fine-tuned on texts from finance domain outperforms existing baseline models and provides 86% of accuracy to correctly predict investor sentiment. Our results show that automated investor sentiment classification needs to built with both complex machine learning models and with good quality fiancnial data.",
keywords = "Deep natural language processing, Financial social media, Investor sentiment",
author = "Aitong Zhong and Qiwei Han",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2nd International Conference on Computing and Data Science, CDS 2021 ; Conference date: 28-01-2021 Through 29-01-2021",
year = "2021",
month = jan,
doi = "10.1109/CDS52072.2021.00067",
language = "English",
series = "Proceedings - 2021 2nd International Conference on Computing and Data Science, CDS 2021",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "356--361",
booktitle = "Proceedings - 2021 2nd International Conference on Computing and Data Science, CDS 2021",
address = "United States",
}