Pith Number

pith:WGLEXUDF

pith:2019:WGLEXUDF5JOQ5VU7BMIOXBIM7J

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FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Dogu Araci

FinBERT adapts a BERT model with financial text to improve sentiment classification on specialized datasets.

arxiv:1908.10063 v1 · 2019-08-27 · cs.CL · cs.LG

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Claims

C1strongest claim

Our results show improvement in every measured metric on current state-of-the-art results for two financial sentiment analysis datasets. We find that even with a smaller training set and fine-tuning only a part of the model, FinBERT outperforms state-of-the-art machine learning methods.

C2weakest assumption

That further pre-training on domain-specific financial corpora plus partial fine-tuning will reliably produce better sentiment classification than general-purpose models or traditional machine-learning baselines when labeled financial data is limited.

C3one line summary

FinBERT adapts BERT to the financial domain and outperforms prior state-of-the-art methods on financial sentiment analysis tasks.

References

35 extracted · 35 resolved · 13 Pith anchors

[1] Basant Agarwal and Namita Mittal. 2016. Machine Learning Approach for Sentiment Analysis. Springer International Publishing, Cham, 21–45. https: //doi.org/10.1007/978-3-319-25343-5_3 2016 · doi:10.1007/978-3-319-25343-5_3

[2] Fernando Sánchez-Rada, and Carlos A 2017 · doi:10.1016/j.eswa.2017.02.002

[3] Shallue and Jaehoon Lee and Joe Antognini and Jascha Sohl-Dickstein and Roy Frostig and George E 2018 · arXiv:1811.03600

[4] Li Guo, Feng Shi, and Jun Tu. 2016. Textual analysis and machine leaning: Crack unstructured data in finance and accounting. The Journal of Finance and Data Science 2, 3 (sep 2016), 153–170. https://d 2016 · doi:10.1016/j.jfds.2017.02.001

[5] Universal language model fine-tuning for text classification 2018 · arXiv:1801.06146

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Cited by

26 papers in Pith

FinTruthQA: A Benchmark for AI-Driven Financial Disclosure Quality Assessment in Investor -- Firm Interactions

Bridging Language Models and Financial Analysis

MulFSA: Multi-level Financial Sentiment Analysis Framework for Bond Market

FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information

ACIL: Auto Chain of Thoughts for In-Context Learning

Receipt and verification

First computed	2026-05-17T23:38:50.273963Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b1964bd065ea5d0ed69f0b10eb850cfa77d298c0bc51128e42239ac87d96a86a

Aliases

arxiv: 1908.10063 · arxiv_version: 1908.10063v1 · doi: 10.48550/arxiv.1908.10063 · pith_short_12: WGLEXUDF5JOQ · pith_short_16: WGLEXUDF5JOQ5VU7 · pith_short_8: WGLEXUDF

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/WGLEXUDF5JOQ5VU7BMIOXBIM7J \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: b1964bd065ea5d0ed69f0b10eb850cfa77d298c0bc51128e42239ac87d96a86a

Canonical record JSON

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    "submitted_at": "2019-08-27T07:40:48Z",
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