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

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