Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets (2020.coling-main)
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| Challenge: | Recent dominance of machine learning-based natural language processing methods has overemphasized model accuracies rather than studying the reasons behind their errors. |
| Approach: | They investigate the error patterns of some widely acknowledged sentiment analysis methods in the finance domain. |
| Outcome: | The proposed models are based on the existing models and have important clues for improving them. |
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| Challenge: | This tutorial will provide an overview of financial opinion mining and provide research directions. |
| Approach: | This tutorial will introduce financial opinion mining and examine possible research directions. |
| Outcome: | This tutorial aims to provide an overview of financial opinion mining and figure out research directions. |
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| Challenge: | Sentiment analysis (SA) has been a long-standing research area in natural language processing. |
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Issues and Perspectives from 10,000 Annotated Financial Social Media Data (2020.lrec-1)
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| Challenge: | In the NLP community, many researchers have begun to use machine learning on financial and economic data. |
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Bridging the Data Gap in Financial Sentiment: LLM-Driven Augmentation (2025.acl-srw)
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| Challenge: | Existing datasets that are outdated and inaccurate hinder accuracy of Financial Sentiment Analysis (FSA) . |
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| Challenge: | Recent large language models such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models . however, their applicability and effectiveness in specific domains like finance needs a better understanding . |
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FinEntity: Entity-level Sentiment Classification for Financial Texts (2023.emnlp-main)
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| Challenge: | FinEntity annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news. |
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Sentiment Analysis: It’s Complicated! (N18-1)
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Kian Kenyon-Dean, Eisha Ahmed, Scott Fujimoto, Jeremy Georges-Filteau, Christopher Glasz, Barleen Kaur, Auguste Lalande, Shruti Bhanderi, Robert Belfer, Nirmal Kanagasabai, Roman Sarrazingendron, Rohit Verma, Derek Ruths
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Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models (2023.findings-emnlp)
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| Challenge: | Existing financial PLMs are not pretrained on sufficiently diverse financial data, leading to subpar generalization performance. |
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MultiFin: A Dataset for Multilingual Financial NLP (2023.findings-eacl)
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| Challenge: | Multilingual models are needed to process financial text, which is produced across the world and requires a large dataset. |
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Uncovering Currency Bias and Syntax Gap in Text Embedding Models (2026.findings-acl)
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| Challenge: | Text-embedding models often inherit societal biases, yet the influence of socio-economic markers remains unexplored. |
| Approach: | They propose to identify Currency Bias as a systemic representational limitation in financial AI . they analyze currency embeddings to identify currency identifiers and associative sensitivity . |
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