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

• URL: http://arxiv.org/abs/2301.09279v1
• Date: Mon, 23 Jan 2023 05:32:42 GMT
• Title: StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series
• Authors: Jean Lee, Hoyoul Luis Youn, Josiah Poon, Soyeon Caren Han
• Abstract summary: This paper introduces StockEmotions, a new dataset for detecting emotions in the stock market. It consists of 10,000 English comments collected from StockTwits, a financial social media platform. Unlike existing financial sentiment datasets, StockEmotions presents granular features such as investor sentiment classes, fine-grained emotions, emojis, and time series data.
• Score: 5.892675412951627
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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