StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series
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.
Code (2)
Tasks
Emotion ClassificationMultivariate Time Series ForecastingSentiment AnalysisTime SeriesTime Series AnalysisTime Series ForecastingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Semantically Enriching Investor Micro-blogs for Opinion-Aware Emotion Analysis: A Practical Approach
While sentiment analysis is the staple of financial NLP, capturing the nuances of 'why' behind that sentiment remains a challenge. There have been attempts to address this by analysing investor emotions alongside sentime…
Sentiment AnalysisCausality between investor sentiment and the shares return on the Moroccan and Tunisian financial markets
This paper aims to test the relationship between investor sentiment and the profitability of stocks listed on two emergent financial markets, the Moroccan and Tunisian ones. Two indirect measures of investor sentiment ar…
Social Media Emotions and Market Behavior
I explore the relationship between investor emotions expressed on social media and asset prices. The field has seen a proliferation of models aimed at extracting firm-level sentiment from social media data, though the be…
LLM-Based Financial Sentiment Analysis in Arabic: Evidence from Saudi Markets
Investor sentiment shapes financial markets, yet modeling sentiment in Arabic financial contexts remains challenging due to linguistic complexity and limited resources. We present an Arabic NLP framework for large-scale …
Sentiment AnalysisEntity LinkingLearning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits
Previous studies have shown that investor sentiment indicators can predict stock market change. A domain-specific sentiment lexicon and sentiment-oriented word embedding model would help the sentiment analysis in financi…
Decision MakingSentiment AnalysisWord Embeddings