Amarachi Mbakwe
2024
Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams
Ethan Callanan
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Amarachi Mbakwe
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Antony Papadimitriou
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Yulong Pei
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Mathieu Sibue
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Xiaodan Zhu
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Zhiqiang Ma
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Xiaomo Liu
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Sameena Shah
Proceedings of the Eighth Financial Technology and Natural Language Processing and the 1st Agent AI for Scenario Planning
2022
TweetFinSent: A Dataset of Stock Sentiments on Twitter
Yulong Pei
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Amarachi Mbakwe
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Akshat Gupta
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Salwa Alamir
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Hanxuan Lin
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Xiaomo Liu
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Sameena Shah
Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
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.
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Co-authors
- Yulong Pei 2
- Xiaomo Liu 2
- Sameena Shah 2
- Ethan Callanan 1
- Antony Papadimitriou 1
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