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

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 10k 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.

Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting

“…The setup introduces features that contain sentiment scores extracted from news and a relevant online forum, as well as other stock-related historical information such as the opening, closing, highest, and lowest prices. In [26], a new dataset for stock market emotion detection is presented. The set contains data consisting of 12 fine-grained emotion classes concerning investor emotion.…”