@inproceedings{pei-etal-2022-tweetfinsent,
title = "{T}weet{F}in{S}ent: A Dataset of Stock Sentiments on {T}witter",
author = "Pei, Yulong and
Mbakwe, Amarachi and
Gupta, Akshat and
Alamir, Salwa and
Lin, Hanxuan and
Liu, Xiaomo and
Shah, Sameena",
editor = "Chen, Chung-Chi and
Huang, Hen-Hsen and
Takamura, Hiroya and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.finnlp-1.5",
doi = "10.18653/v1/2022.finnlp-1.5",
pages = "37--47",
abstract = "Stock sentiment has strong correlations with the stock market but traditional sentiment analysis task classifies sentiment according to having feelings and emotions of good or bad. This definition of sentiment is not an accurate indicator of public opinion about specific stocks. To bridge this gap, we introduce a new task of stock sentiment analysis and present a new dataset for this task named TweetFinSent. In TweetFinSent, tweets are annotated based on if one gained or expected to gain positive or negative return from a stock. Experiments on TweetFinSent with several sentiment analysis models from lexicon-based to transformer-based have been conducted. Experimental results show that TweetFinSent dataset constitutes a challenging problem and there is ample room for improvement on the stock sentiment analysis task. TweetFinSent is available at \url{https://github.com/jpmcair/tweetfinsent}.",
}
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<abstract>Stock sentiment has strong correlations with the stock market but traditional sentiment analysis task classifies sentiment according to having feelings and emotions of good or bad. This definition of sentiment is not an accurate indicator of public opinion about specific stocks. To bridge this gap, we introduce a new task of stock sentiment analysis and present a new dataset for this task named TweetFinSent. In TweetFinSent, tweets are annotated based on if one gained or expected to gain positive or negative return from a stock. Experiments on TweetFinSent with several sentiment analysis models from lexicon-based to transformer-based have been conducted. Experimental results show that TweetFinSent dataset constitutes a challenging problem and there is ample room for improvement on the stock sentiment analysis task. TweetFinSent is available at https://github.com/jpmcair/tweetfinsent.</abstract>
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%0 Conference Proceedings
%T TweetFinSent: A Dataset of Stock Sentiments on Twitter
%A Pei, Yulong
%A Mbakwe, Amarachi
%A Gupta, Akshat
%A Alamir, Salwa
%A Lin, Hanxuan
%A Liu, Xiaomo
%A Shah, Sameena
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F pei-etal-2022-tweetfinsent
%X Stock sentiment has strong correlations with the stock market but traditional sentiment analysis task classifies sentiment according to having feelings and emotions of good or bad. This definition of sentiment is not an accurate indicator of public opinion about specific stocks. To bridge this gap, we introduce a new task of stock sentiment analysis and present a new dataset for this task named TweetFinSent. In TweetFinSent, tweets are annotated based on if one gained or expected to gain positive or negative return from a stock. Experiments on TweetFinSent with several sentiment analysis models from lexicon-based to transformer-based have been conducted. Experimental results show that TweetFinSent dataset constitutes a challenging problem and there is ample room for improvement on the stock sentiment analysis task. TweetFinSent is available at https://github.com/jpmcair/tweetfinsent.
%R 10.18653/v1/2022.finnlp-1.5
%U https://aclanthology.org/2022.finnlp-1.5
%U https://doi.org/10.18653/v1/2022.finnlp-1.5
%P 37-47
Markdown (Informal)
[TweetFinSent: A Dataset of Stock Sentiments on Twitter](https://aclanthology.org/2022.finnlp-1.5) (Pei et al., FinNLP 2022)
ACL
- Yulong Pei, Amarachi Mbakwe, Akshat Gupta, Salwa Alamir, Hanxuan Lin, Xiaomo Liu, and Sameena Shah. 2022. TweetFinSent: A Dataset of Stock Sentiments on Twitter. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 37–47, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.