Pith. sign in

REVIEW 4 major objections 5 minor 50 references

Evaluating Financial Sentiment Analysis with Annotators Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper establishes that inserting the human annotators' own instruction sheet into an LLM prompt improves financial sentiment classification accuracy by 5.90 percentage points on average and 9.08 points at best.

desk verdict A real prompt-engineering effect, but the WSBS labels are the load-bearing wall and they're unmeasured. read the letter →

arxiv 2505.07871 v1 pith:BUREGRF2 submitted 2025-05-09 cs.CL cs.AI

A M Muntasir Rahman , Ajim Uddin , Guiling "Grace" Wang This is my paper · ORCID
classification cs.CLcs.AI
keywords financialsentimentanalysislargelanguagemodelsannotators'instructionassistedpromptdesignWallStreetBetsstockpricepredictionconfidence-scoreindexevaluationbenchmarking
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper's central claim is that much of the apparent weakness of large language models on financial sentiment tasks is an evaluation artifact: models are asked to guess positive, negative, or neutral labels without being told how the human annotators defined those classes. To fix this, the paper introduces the Annotators' Instruction Assisted Prompt (AIAP), which inserts the full annotation instruction—definition, market grounding, and one example per class—into the prompt before the model classifies text. On a new WallStreetBets-derived dataset called WSBS, this one prompt change raises accuracy by 5.90 percentage points on average and 9.08 points in the best configuration across three instruction-tuned LLMs. The paper further shows that sentiment features built from these predictions, including a confidence-score-based index, improve next-day stock price prediction for several heavily discussed tickers. If the result holds, standard financial sentiment benchmarks have been understating LLM ability, and a cheap, model-agnostic prompt alignment can recover much of the gap.

What carries the argument

The central object is the Annotators' Instruction Assisted Prompt (AIAP): a template that takes a minimal classification request ('What is the sentiment of this input? Please choose an answer from negative/neutral/positive') and appends the three-part instruction sheet given to WSBS annotators—a definition for each sentiment class, a grounding that aligns positive/negative/neutral with bullish/bearish/stable market language, and one unambiguous example per class. It works as an in-context task re-definition, turning an under-specified sentiment question into one whose label semantics match the gold annotations. The supporting machinery is the WSBS dataset, whose gold labels were produced under that same instruction, plus a confidence-score-based sentiment scoring method (CSBS) that converts classifier probability outputs into daily sentiment features for stock prediction.

What would settle it

Compare AIAP's accuracy gain on the 1,509 WSBS samples with unanimous first-pass labels against its gain on the samples resolved by majority vote; if the gain is concentrated in the conflict-resolved samples, the prompt is teaching the label-resolution rule rather than improving sentiment judgment. A second check: rewrite the instruction with the same definitions but different examples; if the gain disappears, the examples, not the task definition, are doing the work.

Watch

Extended reading notes

Core claim

The central claim is that a frozen LLM's financial sentiment judgments converge with human annotators' judgments when the model is handed the same written task definition the annotators used. The demonstration rests on WSBS, a dataset of 2,920 WallStreetBets posts and comments labeled by five annotators under a three-part instruction—definition, market grounding, and one example per class—with 1,509 samples receiving unanimous first-pass labels. Across FinGPT, Llama-3, and GPT-4, the Annotators' Instruction Assisted Prompt (AIAP) beats a minimal base prompt on both the unanimous and full subsets; the average gain is 5.90 percentage points, the largest single gain is 9.08 percentage points, and the best overall accuracy rises from 74.95% to 80.91%. Stepwise additions of the three instruction components raise accuracy in most configurations, while ordinary few-shot prompting with the same examples does not match AIAP, and applying the dataset-specific AIAP to a different financial news benchmark yields little gain. The paper takes this as evidence that the improvement transfers the annotators' label semantics rather than coming from a lucky prompt phrasing.

Load-bearing premise

The load-bearing premise is that the WSBS gold labels are a reliable measure of financial sentiment; if those labels are noisy or already encode the instruction, the measured gains may reflect the model replaying the annotation rule instead of understanding sentiment better.

Editorial extensions

If this is right

  • Evaluation practice for financial sentiment benchmarks should change: when annotator instructions exist, they belong in the prompt, or benchmark scores will keep understating LLM ability.
  • The effect is not tied to one model: every tested instruction-tuned LLM improves with AIAP, with the largest single gain reaching 9.08 percentage points on the All-Agree WSBS subset.
  • The component ablation shows the instruction is doing real work—definition alone helps, grounding adds more, and the example completes the effect—while plain few-shot prompting with the same examples does not reproduce the gain.
  • Frozen models can be improved without retraining: AIAP is purely a prompt change, so any instruction-following model can adopt it in evaluation or production.
  • Stock prediction gains are concentrated where WallStreetBets attention is concentrated: sentiment features reduce next-day price prediction error most for GME and AMC, and far less for a broad index or a thinly discussed stock.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural boundary the paper does not fully close: because the same instruction produced the gold labels and the AIAP prompt, part of the measured gain could be protocol mimicry rather than deeper sentiment understanding; a cleaner control would compare AIAP with a differently worded instruction that preserves the same label meanings.
  • The grounding component ties each label to expected market direction, so the prompt may reward models for detecting directional cues rather than affective tone; a testable extension would replace 'bullish/bearish/stable' with neutral wording and measure the drop.
  • The confidence-score sentiment index (CSBS) is a general recipe—neutral predictions get a signed weight from the positive-minus-negative probability gap—so it could be applied to any classifier on other text streams to see whether the stock-prediction gains replicate outside WallStreetBets.
  • If the same annotator-prompt alignment pattern appears on other subjective tasks such as stance or emotion, AIAP would generalize from a financial benchmark trick into a general evaluation principle: give the model the labeler's written definition, not a generic paraphrase.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes Annotators' Instruction Assisted Prompting (AIAP), which inserts the exact annotation instruction given to human annotators (definition, grounding, and example) into the base classification prompt for financial sentiment analysis. The authors introduce a new WallStreetBets sentiment dataset (WSBS) with two versions (Full and All-Agree) and evaluate FinGPT-SA, Llama-3, and GPT-4 in inference mode across three identifier terms ('news', 'tweet', 'input'). They report an average accuracy gain of 5.90% and a best-case gain of 9.08% over a base prompt, with a component ablation showing incremental improvement. They also compare against few-shot prompting, show a near-null result on Financial PhraseBank, and apply the sentiment predictions to stock price prediction using a quantitative score (QuantSS) and a proposed confidence-score-based method (CSBS). The main quantitative claims are that AIAP consistently improves LLM sentiment accuracy on WSBS and that CSBS improves stock prediction over baselines in some settings.

Significance. If the accuracy gains are robust, the paper offers a lightweight and reproducible intervention (adding annotator instructions to prompts) that could improve financial sentiment evaluation, along with a new public dataset. The consistency of gains across 18 model-dataset-identifier cells, the monotonic component ablation, and the negative control on Financial PhraseBank are strengths that go beyond a single anecdotal result. However, the external significance of the central claim rests entirely on the reliability of the WSBS gold labels, and the paper provides no inter-annotator agreement statistic. Because the AIAP prompt contains the very instruction used to create those labels, the measured gain could largely reflect rubric-matching on a self-consistent label set rather than improved sentiment understanding. The application section also shows mixed stock-prediction results that are more suggestive than conclusive. The central idea is plausible and worth publishing after the label-reliability issue is addressed.

major comments (4)
  1. [§2.1, §2.2, Table 2] The load-bearing assumption of the paper is that the WSBS majority-vote labels are a reliable ground truth for financial sentiment, but no inter-annotator agreement statistic (e.g., Fleiss' kappa, Krippendorff's alpha) is reported. The subsection title is 'Annotators' Agreement,' yet only the proportion of unanimous first-pass labels (1509/2920, 51.7%) is given, and the conflict-resolution stage is described incompletely. Since AIAP inserts the exact annotation instruction from Figure 1 into the prompt, the model is handed the labeling rubric at test time. Without a measure of label reliability, the reported gains of +5.90% average and +9.08% best case may reflect the model reproducing that specific rubric on a label set whose consistency is unknown. A concrete test would be to compute agreement on the first-pass double annotations and, if it is low, to re-evaluate on a held-out set of independently produced labels.
  2. [§3.1 vs. §4, Table 2] There is a substantive inconsistency in the model used: §3.1 lists 'Meta-Llama-3-70B-Instruct' as the Llama-3 model, while Table 2's header and the surrounding text refer to 'Meta-Llama3-8B-Instruct.' This matters because the claims about 'Llama-3' gains (e.g., +7.56% on All-Agree with 'news') are attributed to a model family without specifying which variant was actually run. If the experiments used the 8B model, the statements about Llama-3 should say so explicitly, and if the 70B model was used, the table header must be corrected. The paper should clarify this before the results can be interpreted.
  3. [§4, Table 2] No significance tests, confidence intervals, or repeated runs are provided for any of the 18 accuracy comparisons. The headline average (+5.90%) and best case (+9.08%) are point estimates, and the best case is the maximum over three identifier terms, which should be flagged as a selection over multiple comparisons. Although the consistency of all positive gains is encouraging, a paired test (e.g., McNemar's test) or bootstrap confidence intervals for at least the main model–dataset comparisons would establish whether the gains exceed sampling noise, especially for the smaller All-Agree subset.
  4. [§5.4, §5.5, Table 6] The stock-prediction claims are only partially supported by the reported numbers. The paper states that CSBS 'can extract more value out of models that were not trained on WSB's texts compared to the quantitative approach,' but Table 6 shows that for BERT-FPB the average RMSE improvement is nearly identical between CSBS (2.55) and QuantSS (2.39), and for several individual stocks the differences are negative. The conclusion that BERT-WSBS has 'the most advantage' over other fine-tuning datasets is also based on small and variable margins. The application section should be framed more cautiously, with the per-stock variability acknowledged as limiting the strength of the CSBS claim.
minor comments (5)
  1. [Abstract and §1] The abstract states 'improvements up to 9.08' without a percent sign; also, §1 says the dataset and code 'will be released upon acceptance' while the abstract says the dataset is 'available at' a GitHub URL. These statements should be reconciled.
  2. [§2.1] The annotation process is described as 'two different annotations from different annotators' in the initial stage, but the reader is not told how the 2,920 samples were split between the two stages or how 'majority voting' operated when only two annotators examined a sample. Please clarify the mechanics of the conflict-resolution step.
  3. [§4.2, Table 3] The few-shot comparison is performed only on FinGPT with one identifier ('news') and up to three shots; the conclusion that 'few-shot prompting cannot improve performance' is too strong given this limited coverage. It would be more precise to say that these few-shot configurations did not improve FinGPT's performance.
  4. [§4.3] The claim that AIAP is not a 'lottery prompt' is supported by the FPB null result, but the FPB test uses only FinGPT and the best-case improvement from 86.0% to 86.4% is within typical run-to-run variation. The sentence should note that this is a single-model best case, not a rigorous falsification test.
  5. [General] There are several typos and formatting issues, including 'Pubished' in the affiliation footer, inconsistent capitalization of 'Tweet' in Figure 2, 'FInancial' in reference [8], and the unspecified claim that GPT-4 has '1.76 trillion parameters' without a citation.

Circularity Check

2 steps flagged · score 4.0 of 10

AIAP gain on WSBS is partly built in: the prompt and the gold labels share the same annotation instruction, though controls and the FPB null keep the claim partially independent.

  1. self definitional [Section 2.1–2.2 and Section 1 (headline accuracy claim)]
    "For the AIAP, we simply insert the instruction provided to the annotators shown in figure 1 into the base prompt in the following manner. ... The annotation was completed in two stages. In the initial stage, every sample received two different annotations from different annotators. In the final stage, two annotators re-examined each sample, and conflicts were resolved by majority voting. ... This essentially produced two sets of our WallStreetBets-Sentiment Analysis (WSBS) dataset: Full Dataset, All-Agree Dataset."

    The WSBS labels are produced by annotators applying Figure 1, and AIAP inserts Figure 1 verbatim into the model prompt. The benchmark therefore measures how well the model re-applies the very rule that generated the ground truth. A gain in accuracy is expected by construction whenever the model follows instructions, so the headline +5.90% average and +9.08% best-case improvement is not an independent test of financial sentiment understanding; it is partly a self-consistency check between the prompt and the labeling rule. The few-shot comparison and the Financial PhraseBank null result provide some independent support, but the main WSBS claim is constructed to reward rubric-matching.

  2. self definitional [Section 4.1, 'Better Instructions Ensure Better Outcomes']
    "Does the model’s performance gradually increase as the instruction gets better and more aligned with the annotators? Essentially, we hypothesize that the model’s performance should gradually decrease if it does not have access to the entire instruction. ... This confirms our assumption that LLMs’ performance indeed increases step by step as the instruction becomes more enhanced and aligned with the annotators’."

    The ablation adds the Definition, Grounding, and Example components of Figure 1 one at a time to the prompt. Since WSBS labels were produced with the complete Figure 1 instruction, each added component moves the prompt closer to the exact label-generation rule, so the monotone rise in agreement is a direct consequence of the benchmark construction rather than an independent validation of the prompting method. The paper presents this trend as evidence that the main gain is not noise, but the trend is the same self-referential mechanism as the headline result.

full rationale

The central claim is the WSBS accuracy improvement from AIAP. The paper defines the ground truth in Section 2.1 as labels assigned by annotators following the instruction in Figure 1, and then in Section 2.2 constructs AIAP by inserting that same Figure 1 instruction into the prompt. Thus the evaluation is same-source: the prompt contains the annotation rubric and the labels are outputs of that rubric. This makes part of the measured gain expected by construction; it is an instruction-following check more than an externally grounded test of financial sentiment understanding. The paper does include several controls that provide independent content: the few-shot prompting comparison in Section 4.2 shows the gain is not merely from examples, the component ablation in Section 4.1 tests monotonicity (though that trend is also construction-driven), and Section 4.3 reports only a negligible improvement on the external Financial PhraseBank dataset (86.0% to 86.4%), indicating the effect is dataset-specific. There is no fitted parameter renamed as a prediction and no load-bearing self-citation chain. Therefore the circularity is partial rather than total: the benchmark and prompt share the same annotation source, but the paper's few-shot and external-dataset checks prevent the claim from collapsing entirely into its own definition. Score 4 reflects one significant self-referential design element with meaningful independent controls.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim is empirical and does not introduce mathematical free parameters or new entities. The key domain assumptions are the validity of WSBS labels, the non-leakage of the annotation rubric, and the predictive value of WSB sentiment for stock prices.

assumptions (3)
  • domain assumption WSBS annotator labels, including majority-vote labels, are the correct ground truth for financial sentiment evaluation.
    All accuracy numbers in Section 4 compare models against these labels; only 51.7% were unanimous (Section 2.1), and 80 samples were discarded, so the label set is a constructed artifact.
  • domain assumption Inserting the annotators' instruction into the prompt does not leak dataset-specific labels; the instruction is generic.
    AIAP is the base prompt plus Figure 1's instruction (Section 2.2). The instruction includes examples not drawn from WSBS samples, but it is the same rubric used to create labels, so part of the gain may be by construction.
  • domain assumption Sentiment signals from WallStreetBets posts contain predictive information for next-day close price beyond price and volume.
    The stock prediction experiment in Section 5.3 uses sentiment scores as regression features; no model of market efficiency or causality is given.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Evaluating Financial Sentiment Analysis with Annotators Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy." pith.science (2026). https://pith.science/paper/BUREGRF2

@misc{pith2026250507871,
  author       = {Pith},
  title        = {Pith review of: Evaluating Financial Sentiment Analysis with Annotators Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BUREGRF2}},
  note         = {Machine review of arXiv:2505.07871}
}
read the original abstract

Financial sentiment analysis (FSA) presents unique challenges to LLMs that surpass those in typical sentiment analysis due to the nuanced language used in financial contexts. The prowess of these models is often undermined by the inherent subjectivity of sentiment classifications in existing benchmark datasets like Financial Phrasebank. These datasets typically feature undefined sentiment classes that reflect the highly individualized perspectives of annotators, leading to significant variability in annotations. This variability results in an unfair expectation for LLMs during benchmarking, where they are tasked to conjecture the subjective viewpoints of human annotators without sufficient context. In this paper, we introduce the Annotators' Instruction Assisted Prompt, a novel evaluation prompt designed to redefine the task definition of FSA for LLMs. By integrating detailed task instructions originally intended for human annotators into the LLMs' prompt framework, AIAP aims to standardize the understanding of sentiment across both human and machine interpretations, providing a fair and context-rich foundation for sentiment analysis. We utilize a new dataset, WSBS, derived from the WallStreetBets subreddit to demonstrate how AIAP significantly enhances LLM performance by aligning machine operations with the refined task definitions. Experimental results demonstrate that AIAP enhances LLM performance significantly, with improvements up to 9.08. This context-aware approach not only yields incremental gains in performance but also introduces an innovative sentiment-indexing method utilizing model confidence scores. This method enhances stock price prediction models and extracts more value from the financial sentiment analysis, underscoring the significance of WSB as a critical source of financial text. Our research offers insights into both improving FSA through better evaluation methods.

Figures

Figures reproduced from arXiv: 2505.07871 by the authors.

Figure 1
Figure 1. Instruction for the annotators of the WallStreetBets [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Better instruction-alignment or context-awareness ensures better outcome. Gradual performance increase as LLMs have [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

50 extracted references · 20 canonical work pages

  1. [1]

    Werner Antweiler and Murray Z Frank. 2004. Is all that talk just noise? The information content of internet stock message boards.The Journal of finance59, 3 (2004), 1259–1294

    2004

  2. [2]

    Dogu Araci. 2019. Finbert: Financial sentiment analysis with pre-trained language models.arXiv preprint arXiv:1908.10063(2019)

    arXiv 2019

  3. [3]

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners.Advances in neural information processing systems33 (2020), 1877–1901

    2020

  4. [4]

    Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. InProceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794

    2016

  5. [5]

    Yulin Chen, Ning Ding, Xiaobin Wang, Shengding Hu, Hai-Tao Zheng, Zhiyuan Liu, and Pengjun Xie. 2023. Exploring Lottery Prompts for Pre-trained Language Models.arXiv preprint arXiv:2305.19500(2023)

    work page Pith review arXiv 2023

  6. [6]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding.arXiv preprint arXiv:1810.04805(2018)

    arXiv 2018

  7. [7]

    Abhimanyu Dubey. 2024. The Llama 3 Herd of Models. arXiv:2407.21783 [cs.AI] https://arxiv.org/abs/2407.21783

    arXiv 2024

  8. [8]

    FInancial Question Answering and Analysis (FIQA). [n. d.]. FInancial Ques- tion Answering and Analysis (FIQA). https://sites.google.com/view/fiqa/home. Accessed: 2024-05-29

    work page 2024

Show all 50 references
  1. [9]

    Sandro Gössi, Ziwei Chen, Wonseong Kim, Bernhard Bermeitinger, and Siegfried Handschuh. 2023. FinBERT-FOMC: Fine-Tuned FinBERT Model with Sentiment Focus Method for Enhancing Sentiment Analysis of FOMC Minutes. InProceed- ings of the Fourth ACM International Conference on AI i...

    2023

  2. [10]

    Elaine Henry. 2006. Market reaction to verbal components of earnings press releases: Event study using a predictive algorithm.Journal of Emerging Technolo- gies in Accounting3, 1 (2006), 1–19

    2006

  3. [11]

    Elaine Henry. 2008. Are investors influenced by how earnings press releases are written?The Journal of Business Communication (1973)45, 4 (2008), 363–407

    1973

  4. [12]

    Joshua Zoen Git Hiew, Xin Huang, Hao Mou, Duan Li, Qi Wu, and Yabo Xu. 2019. BERT-based financial sentiment index and LSTM-based stock return predictability. arXiv preprint arXiv:1906.09024(2019)

    2019 arXiv

  5. [13]

    Allen H Huang, Amy Y Zang, and Rong Zheng. 2014. Evidence on the information content of text in analyst reports.The Accounting Review89, 6 (2014), 2151–2180

    2014

  6. [14]

    Colm Kearney and Sha Liu. 2014. Textual sentiment in finance: A survey of methods and models.International Review of Financial Analysis33 (2014), 171–185. https://doi.org/10.1016/j.irfa.2014.02.006

    2014 doi

  7. [15]

    Mathias Kraus and Stefan Feuerriegel. 2017. Decision support from financial disclosures with deep neural networks and transfer learning.Decision Support Systems104 (2017), 38–48. https://doi.org/10.1016/j.dss.2017.10.001

    2017 doi

  8. [16]

    Feng Li. 2010. The information content of forward-looking statements in corporate filings—A naïve Bayesian machine learning approach.Journal of Accounting Research48, 5 (2010), 1049–1102

    2010

  9. [17]

    Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Ko- cetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al

  10. [18]

    Yinheng Li, Shaofei Wang, Han Ding, and Hang Chen. 2023. Large Language Models in Finance: A Survey. InProceedings of the Fourth ACM International Conference on AI in Finance. 374–382

    2023

  11. [19]

    Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. 2020. K-bert: Enabling language representation with knowledge graph. InProceedings of the AAAI Conference on Artificial Intelligence, V ol. 34. 2901–2908

    2020

  12. [20]

    Zhuang Liu, Degen Huang, Kaiyu Huang, Zhuang Li, and Jun Zhao. 2021. Finbert: A pre-trained financial language representation model for financial text mining. In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intellige...

    2021

  13. [21]

    I just like the stock

    Suwan Long, Brian Lucey, Ying Xie, and Larisa Yarovaya. 2023. “I just like the stock”: The role of Reddit sentiment in the GameStop share rally.Financial Review58, 1 (2023), 19–37

    2023

  14. [22]

    Tim Loughran and Bill McDonald. 2011. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks.The Journal of finance66, 1 (2011), 35–65

    2011

  15. [23]

    Dakuan Lu, Jiaqing Liang, Yipei Xu, Qianyu He, Yipeng Geng, Mengkun Han, Yingsi Xin, Hengkui Wu, and Yanghua Xiao. 2023. BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark.arXiv preprint arXiv:2302.09432(2023)

    2023 arXiv

  16. [24]

    Bernhard Lutz, Nicolas Pröllochs, and Dirk Neumann. 2018. Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learning. arXiv:1901.00400 [cs.IR]

    2018 arXiv

  17. [25]

    Macedo Maia, André Freitas, and Siegfried Handschuh. 2018. FinSSLx: A Senti- ment Analysis Model for the Financial Domain Using Text Simplification. In2018 IEEE 12th International Conference on Semantic Computing (ICSC). 318–319. https://doi.org/10.1109/ICSC.2018.00065

    2018

  18. [26]

    Pekka Malo, Ankur Sinha, Pekka Korhonen, Jyrki Wallenius, and Pyry Takala

  19. [27]

    Andrew McCallum. 1996. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering.http://www. cs. cmu. edu/-mccallum/bow (1996)

    1996

  20. [28]

    OpenAI. 2024. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL] https: //arxiv.org/abs/2303.08774 Evaluating Financial Sentiment Analysis with Annotators’ Instruction Assisted Prompting Pubished in ICAIF Workshops ’24, Nov 14–16, 2024, Brooklyn, NY

    2024 arXiv

  21. [29]

    Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog1, 8 (2019), 9

    2019

  22. [30]

    Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Dis- tilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.CoRR abs/1910.01108 (2019). arXiv:1910.01108 http://arxiv.org/abs/1910.01108

    2019 arXiv

  23. [31]

    Aliaksei Severyn and Alessandro Moschitti. 2015. Twitter sentiment analysis with deep convolutional neural networks. InProceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 959–962

    2015

  24. [32]

    Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. InProceedings of the 2013 conference on empirical methods in natural language ...

    2013

  25. [33]

    Sahar Sohangir, Dingding Wang, Anna Pomeranets, and Taghi M Khoshgoftaar

  26. [34]

    Philip J Stone, Dexter C Dunphy, and Marshall S Smith. 1966. The general inquirer: A computer approach to content analysis. (1966)

    1966

  27. [35]

    Shan Suthaharan and Shan Suthaharan. 2016. Support vector machine.Machine learning models and algorithms for big data classification: thinking with examples for effective learning(2016), 207–235

    2016

  28. [36]

    InternLM Team. 2023. Internlm: A multilingual language model with progres- sively enhanced capabilities

    2023

  29. [37]

    twitter. 2024. twitter-financial-news-sentiment). https://huggingface.co/datasets/ zeroshot/twitter-financial-news-sentiment Accessed: 2024-29-05

    2024

  30. [38]

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need.Advances in neural information processing systems30 (2017)

    2017

  31. [39]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models.Advances in Neural Information Processing Systems35 (2022), 24824–24837

    2022

  32. [40]

    Pedram Babaei William Todt, Ramtin Babaei. 2023. Fin-LLAMA: Efficient Finetuning of Quantized LLMs for Finance. https://github.com/Bavest/fin-llama

    2023

  33. [41]

    Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebas- tian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann

  34. [42]

    Qianqian Xie, Weiguang Han, Xiao Zhang, Yanzhao Lai, Min Peng, Alejandro Lopez-Lira, and Jimin Huang. 2023. PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance.arXiv preprint arXiv:2306.05443 (2023)

    2023 arXiv

  35. [43]

    Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang. 2023. FinGPT: Open- Source Financial Large Language Models.arXiv preprint arXiv:2306.06031 (2023)

    2023

  36. [44]

    Yi Yang, Mark Christopher Siy Uy, and Allen Huang. 2020. Finbert: A pretrained language model for financial communications.arXiv preprint arXiv:2006.08097 (2020)

    2020 arXiv

  37. [45]

    Bloomberggpt: A large language model for finance.arXiv preprint arXiv:2303.17564(2023)

    2023 arXiv

  38. [46]

    Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2...

    2022 arXiv

  39. [49]

    Boyu Zhang, Hongyang Yang, Tianyu Zhou, Muhammad Ali Babar, and Xiao- Yang Liu. 2023. Enhancing financial sentiment analysis via retrieval augmented large language models. InProceedings of the Fourth ACM International Confer- ence on AI in Finance. 349–356

    2023

  40. [2014]

    Journal of the Association for Information Science and Technology65, 4 (2014), 782–796

    Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology65, 4 (2014), 782–796

    2014

  41. [2018]

    Big Data: Deep Learning for financial sentiment analysis.Journal of Big Data5, 1 (2018), 1–25

    2018

  42. [2023]

    StarCoder: may the source be with you!arXiv preprint arXiv:2305.06161 (2023)

    2023 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.