Paper Citation Record · LEDGER

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning

As of 13 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2412.09859.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.09859 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:42:52.828305Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact0
verified fuzzy22
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ae5218e9-4cb4-4e70-8ce4-2d1ffa8bafa8 · outbound

This paper cites FinBERT: Financial Sentiment Analysis with Pre-trained Language Models.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T16:42:52.634376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc28a123-e2b9-40b0-ac8b-8341685a4448 · outbound

This paper cites Instruction Finetuning Foundation Models, Three-Stage Bubble Analysis, and Exam- ining the Size Effect.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Instruction Finetuning Foundation Models, Three-Stage Bubble Analysis, and Exam- ining the Size Effect

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.504877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3ac6e1a2-e945-4279-9f07-9c40ea60774b · outbound

This paper cites Capital asset pricing model with size factor and normalizing by volatility index, 2024.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Capital asset pricing model with size factor and normalizing by volatility index, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.486587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eb9821f2-52ae-48de-a397-18ce50e69038 · outbound

This paper cites Enriching word vectors with subword information.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Enriching word vectors with subword information

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.465558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 78b51a00-9e63-4ebd-904b-24dea4c0dac5 · outbound

This paper cites A comprehensive study on lexicon based approaches for sentiment analysis.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning A comprehensive study on lexicon based approaches for sentiment analysis

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.445888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f886714b-3f09-4c9b-aac3-ba894472ab41 · outbound

This paper cites AugGPT: Leveraging ChatGPT for Text Data Augmentation.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning AugGPT: Leveraging ChatGPT for Text Data Augmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T16:42:52.671121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61c09cc7-703b-48ed-b714-026dcdece67b · outbound

This paper cites an unresolved cited work.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-11T16:42:53.428243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5f542324-0182-4b9d-aa02-d7f0d85d830a · outbound

This paper cites Bert: Pre-training of deep bidi- rectional transformers for language understanding, 2019.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Bert: Pre-training of deep bidi- rectional transformers for language understanding, 2019

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.409327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e83b4f32-6908-4228-b87e-8d41ab1015e4 · outbound

This paper cites A holistic lexicon-based approach to opinion mining.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning A holistic lexicon-based approach to opinion mining

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.389131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6d13519b-8a0a-44b2-a2e6-e9b213df73a8 · outbound

This paper cites The capital asset pricing model: an overview of the theory.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning The capital asset pricing model: an overview of the theory

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.367869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3ba3b20e-c6b8-4da4-9b90-07000069dc08 · outbound

This paper cites Random walks in stock market prices.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Random walks in stock market prices

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.346761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b6196dd1-e0a6-4529-9a5c-7f226b8f54e5 · outbound

This paper cites Fully automatic lexicon expansion for domain-oriented senti- ment analysis.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Fully automatic lexicon expansion for domain-oriented senti- ment analysis

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.326936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3c786099-ea97-47e9-8d22-fd10d0e3663e · outbound

This paper cites Decision support from financial disclosures with deep neural networks and transfer learning.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Decision support from financial disclosures with deep neural networks and transfer learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.308065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3951c01d-d375-4bec-ae58-80deb38cdd7a · outbound

This paper cites FinGPT: Democratizing Internet-scale Data for Financial Large Language Models.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning FinGPT: Democratizing Internet-scale Data for Financial Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T16:42:52.717746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cd60b0c-36d8-483d-a1aa-c16cae318a3f · outbound

This paper cites Finsslx: A sentiment analysis model for the financial domain using text simplification.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Finsslx: A sentiment analysis model for the financial domain using text simplification

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.288671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f8a141f2-2d72-496e-8d85-ad34d57451dc · outbound

This paper cites Good debt or bad debt: Detecting semantic orientations in economic texts.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Good debt or bad debt: Detecting semantic orientations in economic texts

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T16:42:52.730359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a95a2e1d-5f4c-40ec-93ca-a943b47ba5ea · outbound

This paper cites Delta tfidf: An improved feature space for sentiment analysis.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Delta tfidf: An improved feature space for sentiment analysis

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.251600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation db4621b7-39a5-4bdf-b443-29f3b48f1d3f · outbound

This paper cites Distributed representations of words and phrases and their compositionality.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Distributed representations of words and phrases and their compositionality

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.228791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 83943218-aae9-4a64-8252-e7eef057f70e · outbound

This paper cites Evaluation of sentiment analysis in finance: from lexicons to transformers.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Evaluation of sentiment analysis in finance: from lexicons to transformers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.208430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 56c9b87b-6bd2-4546-a2ed-567eb550ec53 · outbound

This paper cites Glove: Global vectors for word rep- resentation.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Glove: Global vectors for word rep- resentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.185245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e4596ea8-7dab-4d80-8209-dfd0c2e67b20 · outbound

This paper cites Financial news dataset from bloomberg and reuters.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Financial news dataset from bloomberg and reuters

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.165468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 583fd37a-306c-4d42-82d8-030c18a573ac · outbound

This paper cites Improving language under- standing by generative pre-training.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Improving language under- standing by generative pre-training

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T16:42:52.765303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0d041be-7ebc-4f7d-802b-9c2d4f35d611 · outbound

This paper cites An overview of lexicon-based approach for sentiment analysis.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning An overview of lexicon-based approach for sentiment analysis

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.129955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c2ef6cd0-8ed6-41b5-82ac-a351c984a6d7 · outbound

This paper cites Detecting formal thought disorder by deep contextualized word representations.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Detecting formal thought disorder by deep contextualized word representations

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.107214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 277f66e2-c892-4cd7-ba3b-656c98487751 · outbound

This paper cites Big data: Deep learning for financial sentiment analysis.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Big data: Deep learning for financial sentiment analysis

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.088867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4cb42ff3-c85a-400f-ba3b-149bfad55759 · outbound

This paper cites Lexicon-based methods for sentiment analysis.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Lexicon-based methods for sentiment analysis

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.066063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df97eeee-7fcd-4740-a85c-f589319b50df · outbound

This paper cites Does Synthetic Data Generation of LLMs Help Clinical Text Mining?.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T16:42:52.795877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ca803eb-c34d-4c03-ada4-4e6755a868fe · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning LLaMA: Open and Efficient Foundation Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T16:42:52.803866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b80719f-eb91-44b7-b4d2-fb39642327c2 · outbound

This paper cites Classification of sentiment reviews using n-gram machine learning approach.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Classification of sentiment reviews using n-gram machine learning approach

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.046469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 41129211-f9dc-4dfd-97ca-7b99cd2e7713 · outbound

This paper cites Attention is all you need.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Attention is all you need

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:42:53.019913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e14a8916-c6bd-4da8-89aa-336cfd74099e · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning BloombergGPT: A Large Language Model for Finance

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T16:42:52.822565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af4f4a6c-acfb-4526-8355-817f700338b9 · outbound

This paper cites Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models.

Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T16:42:52.828305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.