StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series

Jean Lee, Hoyoul Luis Youn, Josiah Poon and Soyeon Caren Han

Papers from arXiv.org

Abstract: There has been growing interest in applying NLP techniques in the financial domain, however, resources are extremely limited. This paper introduces StockEmotions, a new dataset for detecting emotions in the stock market that consists of 10,000 English comments collected from StockTwits, a financial social media platform. Inspired by behavioral finance, it proposes 12 fine-grained emotion classes that span the roller coaster of investor emotion. Unlike existing financial sentiment datasets, StockEmotions presents granular features such as investor sentiment classes, fine-grained emotions, emojis, and time series data. To demonstrate the usability of the dataset, we perform a dataset analysis and conduct experimental downstream tasks. For financial sentiment/emotion classification tasks, DistilBERT outperforms other baselines, and for multivariate time series forecasting, a Temporal Attention LSTM model combining price index, text, and emotion features achieves the best performance than using a single feature.

Date: 2023-01, Revised 2023-02
New Economics Papers: this item is included in nep-big and nep-cmp
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