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.

介绍了 StockEmotions 数据集，其中包括股票市场的情感情绪分类和时间序列数据，通过 NLP 技术和 LSTM 模型处理，可以提高金融情感分类和多元时间序列预测的性能。