mlsanityData to 14 Oct 2026

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

arXiv 2301.09279 ↗First posted 23 Jan 2023cs.CL cs.AI cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 13 of its 43 references that are in the map. First posted on arXiv 23 Jan 2023.

Builds on

13 of 43 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  5. 2019Adversarial NLI: A New Benchmark for Natural Language Understandingcited by 807
  6. 2020GoEmotions: A Dataset of Fine-Grained Emotionscited by 486
  7. 2017Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasmcited by 118
  8. 2018EmotionLines: An Emotion Corpus of Multi-Party Conversationscited by 80
  9. 2017Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Predictioncited by 60
  10. 2019Dimensional Emotion Detection from Categorical Emotioncited by 25
  11. 2020Towards Label-Agnostic Emotion Embeddingscited by 9
  12. 2021Uncovering the Limits of Text-based Emotion Detectioncited by 8
1 more
  1. 2021FedNLP: An interpretable NLP System to Decode Federal Reserve Communicationscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 2.

9 papers.

  1. 2024A Survey of Large Language Models in Finance (FinLLMs)cited by 79
  2. 2024Simulating Financial Market via Large Language Model based Agentscited by 25
  3. 2026MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategiescited by 11
  4. 2024A Survey of Financial AI: Architectures, Advances and Open Challengescited by 6
  5. 2025Bridging Language Models and Financial Analysiscited by 4
  6. 2025Towards Temporal Knowledge-Base Creation for Fine-Grained Opinion Analysis with Language Modelscited by 1
  7. 2025Comparing LLMs for Sentiment Analysis in Financial Market Newscited by 0
  8. 2025Multi-Modal Opinion Integration for Financial Sentiment Analysis using Cross-Modal Attentioncited by 0
  9. 2026Semantically Enriching Investor Micro-blogs for Opinion-Aware Emotion Analysis: A Practical Approachcited by 0