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

23 Jan 2023arXiv:2301.09279archive 2025-07-28

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

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.

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Code

adlnlp/stockemotions officialmentioned in papermentioned on GitHub report

OwinoMichael/FinBERT-Sentiment-Analysis-and-MTS-Temp-Att.-LSTM- report

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Tasks

Emotion Classification Multivariate Time Series Forecasting Sentiment Analysis Time Series Time Series Analysis Time Series Forecasting

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StockEmotions

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Methods

Adam Attention Attention Dropout BERT Dense Connections DistilBERT Dropout LSTM Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Sigmoid Activation Softmax Tanh Activation Weight Decay WordPiece

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