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arxiv: 2502.00414 · v2 · submitted 2025-02-01 · 💻 cs.CL

Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments

Hasin Jawad Ali , Ajwad Abrar , S.M. Hozaifa Hossain , M. Firoz Mridha This is my paper

Pith reviewed 2026-05-23 03:41 UTC · model grok-4.3

classification 💻 cs.CL
keywords ideological stance detectionIsrael-Palestine conflictReddit commentslarge language modelsprompt engineeringsocial media polarizationstance classificationMixtral 8x7B
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0 comments X

The pith

The Scoring and Reflective Re-read prompt in Mixtral 8x7B achieves the highest performance across accuracy, precision, recall, and F1-score for classifying ideological stances in Israel-Palestine Reddit comments.

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

The paper assembles a dataset of 9,969 Reddit comments on the Israel-Palestine conflict and manually labels each as Pro-Israel, Pro-Palestine, or Neutral. It then compares traditional machine learning, neural networks, pre-trained language models, and multiple prompt engineering strategies on open-source LLMs to perform this three-way classification. The central result is that one specific prompting approach applied to Mixtral 8x7B outperforms the alternatives on every reported metric. A reader would care because reliable automatic labeling of polarized comments could support quantitative study of how social media discourse shifts during active conflicts. The authors release the full labeled dataset for others to use.

Core claim

By evaluating a range of classification techniques on 9,969 manually labeled Reddit comments spanning October 2023 to August 2024, the paper shows that the Scoring and Reflective Re-read prompt strategy used with Mixtral 8x7B produces the highest accuracy, precision, recall, and F1-score for distinguishing Pro-Israel, Pro-Palestine, and Neutral stances.

What carries the argument

The three-class ideological stance labeling of Reddit comments together with prompt engineering strategies evaluated on the Mixtral 8x7B model.

Load-bearing premise

The three-class manual labeling of comments into Pro-Israel, Pro-Palestine, and Neutral is accurate and consistent enough to serve as ground truth for model evaluation.

What would settle it

Independent re-labeling of a random sample of the comments by multiple annotators yields low inter-annotator agreement, or the Mixtral prompt method shows markedly lower metrics on a fresh collection of comments from the same conflict.

Figures

Figures reproduced from arXiv: 2502.00414 by Ajwad Abrar, Hasin Jawad Ali, M. Firoz Mridha, S.M. Hozaifa Hossain.

Figure 1
Figure 1. Figure 1: Top 5 keywords within the comments February 4, 2025 8/26 [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Time Distribution of Comments Data Processing To prepare the dataset for model training and evaluation, a series of preprocessing steps were performed. These steps ensured that the data were clean, consistent, and in a format suitable for the application of machine learning and deep learning models. The following key techniques were employed during the data processing phase: • Label Encoding: Sentiment lab… view at source ↗
Figure 3
Figure 3. Figure 3: Zero and One Shot Prompting Demonstration performance. This strategy was applied to all LLMs [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Three and Five Shots Prompting Demonstration overlooking subtle contextual cues in its initial classification. We extended this approach to a one-shot setting where it reassesses its classification after being presented with an example during the initial classification. This addition combines the iterative strength of Re-read with the guidance provided in a one-shot setting. Figures 5 and 6 demonstrate the… view at source ↗
Figure 5
Figure 5. Figure 5: Re-read Prompting Demonstration • Meta-Prompting (Self-Critique): Meta-Prompting, in the form of self-critique, refers to a two-step prompting strategy aimed at enhancing the model performance through a refinement process. The approach begins with an initial classification of the input text where it predicts a stance category. After February 4, 2025 14/26 [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Re-read-One Shot Prompting Demonstration that, the model is asked to critique its own classification and provide a refined final classification. The refined classification can be either the initial classification or a changed one [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Meta-Prompting (Self-Critique) Demonstration • Context Extraction: The context extraction strategy was used to identify any contextual elements and embedded stances in the text. The prompt was formulated to be explicit yet open-ended, which may allow the model to effectively capture nuances without adhering to strict guidelines. The prompt allowed for the identification of the textual content first which m… view at source ↗
Figure 8
Figure 8. Figure 8: Context Extraction Demonstration • Scoring and Reflective Re-read: The Scoring and reflective re-read strategy is a two-step prompting approach designed to encourage the model to assess the input text both qualitatively and quantitatively. The method begins with an initial scoring phase followed by a reflective re-read phase to refine the classification. In the initial scoring phase, the model was asked to… view at source ↗
Figure 9
Figure 9. Figure 9: Scoring and Reflective Re-read Demonstration the models, particularly in handling computationally expensive processing. Evaluation Metrics To evaluate the performance of the models implemented for sentiment classification, we employed four widely recognized metrics: Accuracy, Precision (Macro), Recall (Macro), and F1 Score (Macro). These metrics were chosen to ensure a comprehensive analysis, particularly … view at source ↗
read the original abstract

In politically sensitive scenarios like wars, social media serves as a platform for polarized discourse and expressions of strong ideological stances. While prior studies have explored ideological stance detection in general contexts, limited attention has been given to conflict-specific settings. This study addresses this gap by analyzing 9,969 Reddit comments related to the Israel-Palestine conflict, collected between October 2023 and August 2024. The comments were categorized into three stance classes: Pro-Israel, Pro-Palestine, and Neutral. Various approaches, including machine learning, pre-trained language models, neural networks, and prompt engineering strategies for open source large language models (LLMs), were employed to classify these stances. Performance was assessed using metrics such as accuracy, precision, recall, and F1-score. Among the tested methods, the Scoring and Reflective Re-read prompt in Mixtral 8x7B demonstrated the highest performance across all metrics. This study provides comparative insights into the effectiveness of different models for detecting ideological stances in highly polarized social media contexts. The dataset used in this research is publicly available for further exploration and validation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper collects 9,969 Reddit comments on the Israel-Palestine conflict (Oct 2023–Aug 2024) and manually labels them into three stance classes (Pro-Israel, Pro-Palestine, Neutral). It then benchmarks traditional ML classifiers, PLMs, neural networks, and multiple prompt-engineering strategies on open LLMs, reporting that a Scoring + Reflective Re-read prompt on Mixtral 8x7B achieves the highest accuracy, precision, recall, and F1.

Significance. If the ground-truth labels prove reliable, the work supplies a publicly released dataset for conflict-specific stance detection and a head-to-head comparison that highlights prompt-engineering gains over conventional baselines; the dataset release itself supports reproducibility and follow-on research.

major comments (2)
  1. [Dataset Construction / Methods] Dataset section: the manuscript states only that comments “were categorized” into the three classes and supplies no annotation protocol, number of annotators, adjudication procedure, or inter-annotator agreement statistic (Cohen’s/Fleiss’ kappa or percentage agreement). Because every performance number and the ranking of the Mixtral prompt rest on these labels as ground truth, the omission renders the central empirical claims impossible to evaluate.
  2. [Results / Evaluation] Results section: performance differences between methods (including the claimed superiority of Scoring + Reflective Re-read on Mixtral) are presented without any statistical significance test (McNemar, bootstrap, or paired t-test). It is therefore unclear whether the observed metric gaps exceed what would be expected from label noise or sampling variation.
minor comments (1)
  1. [Abstract] Abstract: the sentence “the comments were categorized” should be expanded to mention at least the existence of an annotation protocol and agreement measure so readers can immediately gauge the strength of the evaluation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these constructive comments, which highlight important aspects of transparency and statistical rigor. We will revise the manuscript to address both points fully.

read point-by-point responses

  1. Referee: [Dataset Construction / Methods] Dataset section: the manuscript states only that comments “were categorized” into the three classes and supplies no annotation protocol, number of annotators, adjudication procedure, or inter-annotator agreement statistic (Cohen’s/Fleiss’ kappa or percentage agreement). Because every performance number and the ranking of the Mixtral prompt rest on these labels as ground truth, the omission renders the central empirical claims impossible to evaluate.

    Authors: We agree that the annotation details are essential for assessing label reliability. The submitted manuscript provided only a brief statement on categorization. In the revised version we will insert a new subsection under Methods that fully describes the annotation protocol, number of annotators, annotation guidelines, adjudication process for disagreements, and inter-annotator agreement statistics (Cohen’s kappa and percentage agreement). revision: yes

  2. Referee: [Results / Evaluation] Results section: performance differences between methods (including the claimed superiority of Scoring + Reflective Re-read on Mixtral) are presented without any statistical significance test (McNemar, bootstrap, or paired t-test). It is therefore unclear whether the observed metric gaps exceed what would be expected from label noise or sampling variation.

    Authors: We concur that statistical testing is required to substantiate performance differences. The current manuscript reports raw metrics only. In the revision we will add McNemar’s tests (or bootstrap confidence intervals with p-values) for all pairwise comparisons among the top-performing methods, including the Mixtral prompt, to determine whether observed differences are statistically significant. revision: yes

Circularity Check

0 steps flagged

No circularity; standard empirical evaluation against external labels

full rationale

The paper collects 9,969 Reddit comments, manually assigns them to Pro-Israel/Pro-Palestine/Neutral classes, then reports accuracy/precision/recall/F1 for ML, PLM, NN, and LLM-prompt baselines against those fixed labels. No equations, no fitted parameters renamed as predictions, no self-citation chains, and no derivation that reduces to its own inputs by construction. The evaluation is a straightforward comparison on held-out manual annotations; the absence of IAA details affects validity but does not create circularity under the defined patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central performance claim rests on the assumption that human-assigned stance labels are reliable ground truth and that the collected comments adequately represent polarized discourse.

axioms (1)
  • domain assumption Reddit comments can be reliably and consistently categorized into Pro-Israel, Pro-Palestine, and Neutral by human annotators.
    The study treats these three classes as the evaluation target without reporting annotation details or agreement metrics.

pith-pipeline@v0.9.0 · 5744 in / 1217 out tokens · 26705 ms · 2026-05-23T03:41:25.430757+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

41 extracted references · 41 canonical work pages · cited by 1 Pith paper · 8 internal anchors

  1. [1]

    Political Bias and War

    Jackson MO, Morelli M. Political Bias and War. American Economic Review. 2007;97(4):1353–1373. doi:10.1257/aer.97.4.1353

    work page doi:10.1257/aer.97.4.1353 2007

  2. [2]

    The role of (social) media in political polarization: a systematic review

    Kubin E, von Sikorski C. The role of (social) media in political polarization: a systematic review. Annals of the International Communication Association. 2021;45(3):188–206. doi:10.1080/23808985.2021.1976070

    work page doi:10.1080/23808985.2021.1976070 2021

  3. [3]

    Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies

    Kuila A, Sarkar S. Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies. In: Proceedings of the Second Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2024). ELRA Language Resource Association; 2024. p. 1–11

    work page 2024

  4. [4]

    Sentiment analysis of the United States public support of nuclear power on social media using large language models

    Kwon OH, Vu K, Bhargava N, et al. Sentiment analysis of the United States public support of nuclear power on social media using large language models. Renewable and Sustainable Energy Reviews. 2024;200:114570. doi:10.1016/j.rser.2024.114570

    work page doi:10.1016/j.rser.2024.114570 2024

  5. [5]

    Analysis of Political Sentiment Orientations on Twitter

    Ansari MZ, Aziz MB, Siddiqui MO, Mehra H, Singh KP. Analysis of Political Sentiment Orientations on Twitter. Procedia Computer Science. 2020;167:1821–1828. doi:10.1016/j.procs.2020.03.201. February 4, 2025 22/26

    work page doi:10.1016/j.procs.2020.03.201 2020

  6. [6]

    Predicting political sentiments of voters from Twitter in multi-party contexts

    Khatua A, Khatua A, Cambria E. Predicting political sentiments of voters from Twitter in multi-party contexts. Applied Soft Computing. 2020;97:106743. doi:10.1016/j.asoc.2020.106743

    work page doi:10.1016/j.asoc.2020.106743 2020

  7. [7]

    Political Ideology Detection Using Recursive Neural Networks

    Iyyer M, Enns P, Boyd-Graber J, Resnik P. Political Ideology Detection Using Recursive Neural Networks. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers); 2014. p. 1117–1127

    work page 2014

  8. [8]

    NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles

    Hamborg F, Donnay K. NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics; 2021. p. 1663–1675

    work page 2021

  9. [9]

    Ideology detection using transformer-based machine learning models; 2024

    ¨Ozt¨ urk O,¨Ozcan A. Ideology detection using transformer-based machine learning models; 2024

    work page 2024

  10. [10]

    ParlVote: A Corpus for Sentiment Analysis of Political Debates

    Abercrombie G, Batista-Navarro R. ParlVote: A Corpus for Sentiment Analysis of Political Debates. In: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020). European Language Resources Association (ELRA); 2020. p. 5073–5078

    work page 2020

  11. [11]

    Sentiment analysis on Twitter data towards climate action

    Rosenberg E, Tarazona C, Mallor F, Eivazi H, Pastor-Escuredo D, Fuso-Nerini F, et al. Sentiment analysis on Twitter data towards climate action. Results in Engineering. 2023;19:101287. doi:10.1016/j.rineng.2023.101287

    work page doi:10.1016/j.rineng.2023.101287 2023

  12. [12]

    From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

    Feng S, Park CY, Liu Y, Tsvetkov Y. From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics; 2023. p. 11737–11762

    work page 2023

  13. [13]

    Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategies

    Abrar A, Oeshy NT, Kabir M, Ananiadou S. Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategies. arXiv preprint arXiv:250108441. 2025

    work page 2025

  14. [14]

    Negativity spreads faster: A large-scale multilingual Twitter analysis on the role of sentiment in political February 4, 2025 23/26 communication

    Antypas D, Preece A, Camacho-Collados J. Negativity spreads faster: A large-scale multilingual Twitter analysis on the role of sentiment in political February 4, 2025 23/26 communication. Online Social Networks and Media. 2023;33:100242. doi:10.1016/j.osnem.2023.100242

    work page doi:10.1016/j.osnem.2023.100242 2025

  15. [15]

    An integrated approach for political bias prediction and explanation based on discursive structure

    Ferracane E, Baly R, Martino GDS, Barr´ on-Cede˜ no A, Nakov P. An integrated approach for political bias prediction and explanation based on discursive structure. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers); 2023. p. 11195–11205

    work page 2023

  16. [16]

    CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News Media

    Kim MY, Johnson KM. CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News Media. In: Proceedings of the 29th International Conference on Computational Linguistics; 2022. p. 2780–2793

    work page 2022

  17. [17]

    Topic-specific sentiment analysis can help identify political ideology

    Bhatia S, Deepak P. Topic-specific sentiment analysis can help identify political ideology. In: Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis; 2018. p. 79–84

    work page 2018

  18. [18]

    Analyzing ELMo and DistilBERT on Socio-political News Classification

    B¨ uy¨ ukoz B, H¨ urriyetoglu A,¨Ozg¨ ur A. Analyzing ELMo and DistilBERT on Socio-political News Classification. In: Proceedings of AESPEN 2020, Language Resources and Evaluation Conference (LREC 2020); 2020. p. 9–18

    work page 2020

  19. [19]

    LLMs left, right, and center: Assessing GPT’s capabilities to label political bias from web domains; 2024

    Hernandes R. LLMs left, right, and center: Assessing GPT’s capabilities to label political bias from web domains; 2024

    work page 2024

  20. [20]

    Whose side are you on? Investigating the political stance of large language models

    Pit P, Ma X, Conway M, Chen Q, Bailey J, Pit H, et al. Whose side are you on? Investigating the political stance of large language models. New Media & Society. 2023

    work page 2023

  21. [21]

    Bias Detection of Palestinian/Israeli Conflict in Western Media: A Sentiment Analysis Experimental Study

    Al-Sarraj WF, Lubbad HM. Bias Detection of Palestinian/Israeli Conflict in Western Media: A Sentiment Analysis Experimental Study. In: 2018 International Conference on Promising Electronic Technologies (ICPET). Gaza, Palestine: Islamic University of Gaza; 2018

    work page 2018

  22. [22]

    Taking sides: Public Opinion over the Israel-Palestine Conflict in 2021; 2022

    Imtiaz A, Khan D, Lyu H, Luo J. Taking sides: Public Opinion over the Israel-Palestine Conflict in 2021; 2022. Available from: https://arxiv.org/abs/2201.05961

    work page arXiv 2021

  23. [23]

    Daily Public Opinion on Israel-Palestine War; 2024

    Asaniczka. Daily Public Opinion on Israel-Palestine War; 2024. Kaggle. Available from: https://doi.org/10.34740/KAGGLE/DSV/9367906. February 4, 2025 24/26

    work page doi:10.34740/kaggle/dsv/9367906 2024

  24. [24]

    Measuring nominal scale agreement among many raters

    Fleiss JL. Measuring nominal scale agreement among many raters. Psychological bulletin. 1971;76(5):378

    work page 1971

  25. [25]

    BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

    Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics; 2019. p. 4171–4186

    work page 2019

  26. [26]

    Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network

    Sherstinsky A. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D: Nonlinear Phenomena. 2020;404:132306. doi:10.1016/j.physd.2019.132306

    work page doi:10.1016/j.physd.2019.132306 2020

  27. [27]

    Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction; 2019

    Cui Z, Ke R, Pu Z, Wang Y. Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction; 2019. Available from: https://arxiv.org/abs/1801.02143

    work page arXiv 2019

  28. [28]

    Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks

    Dey R, Salem FM. Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks. In: Circuits, Systems, and Neural Networks (CSANN) Lab. East Lansing, MI, USA: Department of Electrical and Computer Engineering, Michigan State University; 2017

    work page 2017

  29. [29]

    Unsupervised Cross-lingual Representation Learning at Scale

    Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzm´ an F, et al. Unsupervised Cross-lingual Representation Learning at Scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics; 2020.Available from: https://arxiv.org/abs/1911.02116

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  30. [30]

    DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

    Sanh V, Debut L, Chaumond J, Wolf T. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. In: NeurIPS 2019 Workshop on Energy Efficient Machine Learning and Cognitive Computing; 2019.Available from: https://arxiv.org/abs/1910.01108

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  31. [31]

    ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

    Clark K, Luong MT, Le QV, Manning CD. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. In: International Conference on Learning Representations; 2020.Available from: https://arxiv.org/abs/2003.10555. February 4, 2025 25/26

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  32. [32]

    Mistral 7B

    Jiang AQ, Sablayrolles A, Mensch A, Bamford C, Chaplot DS, de las Casas D, et al.. Mistral 7B; 2023. Available from: https://arxiv.org/abs/2310.06825

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  33. [33]

    Mixtral of Experts

    Jiang AQ, Sablayrolles A, Roux A, Mensch A, Savary B, Bamford C, et al.. Mixtral of Experts; 2024. Available from: https://arxiv.org/abs/2401.04088

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  34. [34]

    Gemma: Open Models Based on Gemini Research and Technology

    Team G, Mesnard T, Hardin C, Dadashi R, Bhupatiraju S, Pathak S, et al.. Gemma: Open Models Based on Gemini Research and Technology; 2024. Available from: https://arxiv.org/abs/2403.08295

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  35. [35]

    The Falcon Series of Open Language Models

    Almazrouei E, Alobeidli H, Alshamsi A, Cappelli A, Cojocaru RA, Hesslow D, et al. The Falcon Series of Open Language Models. ArXiv. 2023;abs/2311.16867

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  36. [36]

    Language Models are Few-Shot Learners

    Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al.. Language Models are Few-Shot Learners; 2020. Available from: https://arxiv.org/abs/2005.14165

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  37. [37]

    New Directions in Text Classification Research: Maximizing The Performance of Sentiment Classification from Limited Data

    Agustian S, Syah M, Fatiara N, Abdillah R. New Directions in Text Classification Research: Maximizing The Performance of Sentiment Classification from Limited Data. ArXiv. 2024;abs/2407.05627. doi:10.48550/arXiv.2407.05627

    work page doi:10.48550/arxiv.2407.05627 2024

  38. [38]

    OPEN-AMZPRE : Optimized Preprocessing with Ensemble Classification for Amazon Product Reviews Sentiment Prediction

    Hote P, Pandey D. OPEN-AMZPRE : Optimized Preprocessing with Ensemble Classification for Amazon Product Reviews Sentiment Prediction. International Journal of Scientific Research in Science and Technology. 2023;doi:10.32628/ijsrst52310672

    work page doi:10.32628/ijsrst52310672 2023

  39. [39]

    Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model

    Cahya L, Luthfiarta A, Krisna J, Winarno S, Nugraha A. Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model. Jurnal Nasional Teknologi dan Sistem Informasi. 2024;doi:10.25077/teknosi.v9i3.2023.290-298

    work page doi:10.25077/teknosi.v9i3.2023.290-298 2024

  40. [40]

    Enhancing classification accuracy on code-mixed and imbalanced data using an adaptive deep autoencoder and XGBoost

    Shakith A, Arockiam L. Enhancing classification accuracy on code-mixed and imbalanced data using an adaptive deep autoencoder and XGBoost. The Scientific Temper. 2024;doi:10.58414/scientifictemper.2024.15.3.27

    work page doi:10.58414/scientifictemper.2024.15.3.27 2024

  41. [41]

    Threshold optimization for F measure of macro-averaged precision and recall

    Berger A, Guda S. Threshold optimization for F measure of macro-averaged precision and recall. Pattern Recognition. 2020;102:107250. doi:10.1016/j.patcog.2020.107250. February 4, 2025 26/26

    work page doi:10.1016/j.patcog.2020.107250 2020