Multiclass Sentiment Analysis for Identifying Political Viewpoints

Girma Yohannis Bade; Grigori Sidorov; Jose Luis Oropeza; Olga Kolesnikova

REVIEW 3 major objections 7 minor 36 references

Multiclass Sentiment Analysis for Identifying Political Viewpoints

T0 review · 3 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Tamil political tweets hold best model to 0.28 macro F1 on a seven-way sentiment task.

desk verdict A low-resource shared-task baseline with plausible but internally inconsistent reported numbers; useful only after the tables are fixed and official scores confirmed. read the letter →

arxiv 2608.11049 v1 pith:ZYFWB3J7 submitted 2026-08-11 cs.CL cs.AI

Girma Yohannis Bade , Olga Kolesnikova , Jose Luis Oropeza , Grigori Sidorov This is my paper · ORCID

classification cs.CLcs.AI

keywords multiclasssentimentanalysispoliticaldiscourseTamilXGBoostBERTlow-resourcelanguagessocialmediamacroF1

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

This paper tries to establish a baseline for multiclass sentiment analysis of political tweets in Tamil, a low-resource language. It frames the task as seven-way classification over Substantiated, Sarcastic, Opinionated, Positive, Negative, Neutral, and None-of-the-above, using a labeled set of social media posts. The authors train two pipelines, TF-IDF with XGBoost and a fine-tuned BERT, and report macro-F1 scores of 0.2835 and 0.2806 on the held-out test set. They read these numbers as evidence that political discourse is hard to classify automatically and as a floor that future work must beat. If the result holds, it gives the community a concrete reference point for political opinion mining in under-resourced Dravidian languages.

What carries the argument

The machinery is a seven-class supervised pipeline built on a 4,352-post Tamil training set, with a 544-post development set and a 544-post unlabeled test set. One arm encodes text with TF-IDF and trains an XGBoost classifier; the other tokenizes with BertTokenizer and fine-tunes bert-base-uncased for five epochs at batch size 32 and learning rate 1e-5. The load-bearing quantity is macro-averaged F1 over all seven classes, which is what ranks the models; because macro averaging weighs rare classes equally with common ones, the classifiers' poor handling of rare classes drives the low scores.

What would settle it

Sum the predicted counts in the published confusion matrices by class and compare them with the #Predicted column of Table 6 (305, 84, 50, 43, 15, 23, 24). If the sums do not match, or if recomputing macro F1 from a consistent matrix changes the 0.2835 and 0.2806 figures, the central result is not reproducible from the paper itself.

Watch

Extended reading notes

Core claim

The paper's central claim is that on the official test set, XGBoost with TF-IDF features reaches a macro F1 of 0.2835 and BERT reaches 0.2806, a near tie in which XGBoost edges ahead. Both models are strongly biased toward the Positive class: Positive receives by far the most correct predictions, while Opinionated, Sarcastic, Substantiated, None-of-the-above, and much of Negative are frequently absorbed into it. The authors present these numbers as demonstrating the difficulty of contextualized political sentiment and as baseline results for further work on this dataset and language.

Load-bearing premise

The load-bearing premise is that the published tables all come from one consistent evaluation run of the same test predictions; since the paper's own per-class counts do not fully agree, that premise is not fully established.

Editorial extensions

If this is right

A future system for this task must clear roughly 0.28 macro F1 on the same seven classes to be a meaningful improvement.
The error pattern points to the classes with the smallest training support, None-of-the-above and Substantiated, and to Positive as the dominant confusion target.
Class-balanced training or cost-sensitive losses are the direct next step suggested by the data, not a new architecture.
The near tie between a bag-of-words-plus-boosted-trees model and a fine-tuned transformer implies that architecture is not the main bottleneck on this dataset.

Reading between the lines

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

A reader's inference from the two result tables is that the 0.0029 gap between XGBoost and BERT is smaller than the inconsistency between the reported per-class statistics, so the claimed ordering should not be used as evidence until the numbers are reconciled.
A testable extension the paper leaves implicit is hierarchical classification: first coarse positive/negative/neutral, then fine-grained classes, which could reduce the dominant confusion and is directly checkable on the same test set.
The low scores also suggest a data-limited regime, so data augmentation or pseudo-labeling on the unlabeled test posts might improve rare classes more than changing preprocessing.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

Quick take: this is a DravidianLangTech 2025 system description for seven-way political sentiment classification of Tamil X posts. The only genuinely new artifact is a numeric baseline — XGBoost macro F1 0.2835, BERT 0.2806 — on an externally scored test set. If those numbers survive verification, they give future shared-task participants something to beat. That is the paper's whole contribution, and it is modest but real.

What it does well: the authors use the organizer-supplied train/dev/test split, evaluate on an unlabeled test set through the official channel, and report per-class precision/recall/F1 plus confusion-matrix commentary. That is the right experimental skeleton for a shared task. The limitation statement, apart from the one overclaim mentioned below, is honest about the tiny dataset and generalization limits.

Now the soft spots, in proportion. The biggest one is internal consistency. Section 3.3 says TF-IDF was used for “Logistic Regression,” but Section 3.4 and all results are XGBoost. That looks like a leftover sentence, but it should not be in a published paper. More seriously, Table 4 and Table 6 contradict each other. Using precision = TP/predicted and recall = TP/support, Table 4 implies Opinionated had about 23 predicted instances (0.0870 * 46 / 0.1739), while Table 6 says 305. The stress-test note is right: the Table 6 predicted counts are the same multiset as Table 4's derived predicted counts, but assigned to the wrong labels (305 actually matches Positive's derived count). So one of the tables is mislabeled or permuted. The headline macro F1 comes from Table 4, which is internally consistent with its own accuracy and weighted averages, so the contradiction does not prove the F1 numbers are false — but it does mean the paper as published cannot be trusted without the official results, the predictions, or the confusion matrices.

Other issues: no code, data, or predictions are released, so nothing is independently reproducible. The limitation statement says the model demonstrated “strong performance” when the F1 scores are low, which is an overclaim given the macro F1 around 0.28. Heavy self-citation is annoying but not a scientific flaw; the cited works are relevant enough.

Bottom line: this paper is for the DravidianLangTech community and shared-task track readers who want a numeric reference point. With the internal contradictions fixed and official scores confirmed, it is a citable system description. As it stands, it needs heavy revision before it should appear. I would still send it to peer review rather than desk-reject, because the shared-task evaluation is real and the flaws are fixable — but I would not cite the headline numbers until the authors clean up the tables and release the predictions.

Recommendation: allow peer review, but require the inconsistencies to be resolved and the raw predictions or official leaderboard scores to be included.

Referee Report

3 major / 7 minor

Summary. The paper describes a multiclass sentiment analysis system for political Tamil tweets, developed for the DravidianLangTech 2025 shared task. The authors evaluate two models: an XGBoost classifier with TF-IDF features and a BERT-base-uncased transformer. On the official test set they report macro F1 scores of 0.2835 (XGBoost) and 0.2806 (BERT), concluding that the task is challenging and positioning their results as a baseline for future work.

Significance. If the reported results are accurate, the paper provides a useful, if modest, empirical baseline for political sentiment analysis in a low-resource language (Tamil). The use of standard evaluation metrics and the transparent reporting of BERT hyperparameters are strengths. However, the manuscript contains internal numerical inconsistencies that undermine confidence in the headline F1 scores, and it does not provide code, predictions, or official evaluation materials to support independent verification. The contribution is a system description for a shared task, acceptable in principle for a workshop venue only if the reporting errors are corrected and the results are made verifiable.

major comments (3)

[Section 3.5, Tables 4 and 6] Table 6's predicted-instance counts contradict the precision and recall values in Table 4. From Table 4, the predicted count for each class is recall × support / precision, which gives approximately 23 (Opinionated), 50 (Sarcastic), 24 (Neutral), 305 (Positive), 43 (Substantiated), 84 (Negative), and 15 (None). Table 6 lists these same values but assigned to different labels (Opinionated 305, Sarcastic 84, Neutral 50, Positive 43, Substantiated 15, Negative 23, None 24). The text in Section 3.5 explicitly repeats the erroneous assignment when it states that the Opinionated label received 305 predictions. Because the macro F1 values in Table 4 are derived from the same per-class precision and recall values, this inconsistency makes the paper's internal reporting of the official test results unreliable and leaves the headline F1 scores without corroboration from the published tables. The authors must correct Table 6 and the accompanying discussion, or provide the official confusion matrices and predictions.
[Sections 3.3 and 3.4] Section 3.3 states that TF-IDF and BertTokenizer are applied for the 'Logistic Regression and BERT models,' but Section 3.4 describes XGBoost (XGBClassifier) and BERT, and no Logistic Regression results appear anywhere in the paper. This methodological contradiction must be resolved: either the Logistic Regression experiment should be reported, or the text in Section 3.3 should be corrected to refer to XGBoost. As written, it is unclear which models were actually evaluated.
[Reproducibility and verifiability] The manuscript provides no code, no data, no XGBoost hyperparameters, and no link to the official evaluation scores. Given the internal inconsistency between Tables 4 and 6, the authors should make their test-set predictions or confusion matrices available as supplementary material so that the claimed macro F1 scores (0.2835 and 0.2806) can be independently checked. Without such materials, the central comparative claim is not verifiable.

minor comments (7)

[Section 3.1] The test set is described as 'unlabeled,' yet Tables 4 and 5 report support counts for each class. Please clarify that gold labels were obtained from the shared task organizers after submission, or describe how the support counts were derived.
[Section 3.5] The text says the results of both approaches are presented in 'Tables 4 and 6,' but the BERT results appear in Table 5, not Table 6. The reference should be to Tables 4 and 5 for the two models, with Table 6 as a separate summary for XGBoost.
[Section 3.4] There is a typo in 'IF-IDF' which should be 'TF-IDF.'
[Abstract] The keyword line has a formatting artifact: 'K ˛eywords' displays an unusual accent; this should be cleaned up.
[Section 4] The conclusion contains a duplicated phrase: 'into into seven multiclasses.'
[Section 3.5] There are several typos in the figure discussion, including 'corret' instead of 'correct,' 'osition' instead of 'position,' and 'Figure 2 and 3' should be 'Figures 2 and 3.'
[Section 1] The sentence 'the main contribution of this paper is summarized as follows' should begin with a capital letter after the preceding period.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline F1 scores are external measurements from an organizer-supplied test set; the internal table inconsistency is a correctness issue, not a circular derivation.

full rationale

The paper makes no first-principles derivation or fitted-prediction loop. Its central claim is that XGBoost and BERT achieve macro F1 scores of 0.2835 and 0.2806 on the official DravidianLangTech 2025 test set, which is an external benchmark. The models are trained on labeled data and evaluated on an unseen test set; no target quantity is defined in terms of the result and no fitted parameter is renamed as a prediction. Abundant self-citations in the literature review and methodology are not load-bearing for the test-set F1: the dataset comes from the shared task, not from the authors' own construction, and the cited prior work is background context, not a premise that forces the outcome. The inconsistency between Table 4's per-class precision/recall and Table 6's predicted counts is a reporting/correctness defect, not a form of circularity, because it does not make the F1 value an input to itself. Under the hard rules, no circular step can be exhibited with a quote, so the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim is an empirical F1 measurement, so the paper does not rest on a theoretical derivation. The ledger lists the hand-chosen hyperparameters and the domain assumptions about label quality, metric choice, and cross-lingual transfer. No invented entities are introduced.

free parameters (4)

BERT learning rate = 1e-5
Hand-chosen standard value; not tied to a derivation of the reported F1 score, but affects the measured result.
BERT epochs = 5
Hand-chosen; no early stopping or tuning on the test set is described.
BERT batch size = 32
Hand-chosen standard value.
XGBoost hyperparameters = not specified
The paper does not report any XGBoost hyperparameters (learning rate, max depth, number of estimators), so replication is impossible without contacting the authors.

assumptions (5)

domain assumption The DravidianLangTech 2025 dataset labels are correct and the seven-class scheme is a meaningful operationalization of political viewpoint sentiment.
The paper relies on organizer-provided labels (Section 3.1) without any quality checks.
domain assumption bert-base-uncased, an English pretrained model, transfers to Tamil text without language adaptation.
Section 3.4 selects bert-base-uncased for a Tamil dataset; no Tamil pretraining or multilingual model is used.
domain assumption Macro F1 is the appropriate primary metric for this seven-class task.
The paper ranks based on macro F1 (Section 3.5), a shared task convention.
domain assumption Standard preprocessing (removing punctuation, emojis, user mentions) improves or at least does not harm model performance.
Section 3.2 applies this uniformly without an ablation.
domain assumption The official test set of 544 instances is a sufficient sample to compare models.
Used as the sole evaluation set; no confidence intervals.

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Cite this review

Pith. "Pith review of Multiclass Sentiment Analysis for Identifying Political Viewpoints." pith.science (2026). https://pith.science/paper/ZYFWB3J7

@misc{pith2026260811049,
  author       = {Pith},
  title        = {Pith review of: Multiclass Sentiment Analysis for Identifying Political Viewpoints},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZYFWB3J7}},
  note         = {Machine review of arXiv:2608.11049}
}

read the original abstract

The rapid growth of social media has created vast amounts of political discourse, which provides valuable opportunities to analyze public opinions and identify different political perspectives. Sentiment Analysis (SA) is a core task in Natural Language Processing (NLP) that allows the computational study of attitudes and opinions in textual data, and has become increasingly important for understanding political discourse. In this work, we investigate multiclass sentiment analysis of political view- points on social media, that is to automatically discriminate multiple sentiment classes over political issues and figures. To solve this task we design and evaluate two machine-learning approaches based on XGBoost and BERT. We train and evaluate the models on a labeled dataset of political social media posts using standard classification metrics. The experimental results show that the XGBoost model reaches an F1-score of 0.2835 and the BERT- based model reaches an F1-score of 0.2806 on the test set. These results demonstrate the challenge of classifying complex and contextualized political discourse sentiment and provide a baseline for future research in multiclass political sentiment analysis.

Figures

Figures reproduced from arXiv: 2608.11049 by the authors.

**Figure 1.** The work flow of proposed model 3.5 Results and Discussion The experimental results provide an empirical comparison of the machine learning and transformerbased approaches considered in this study. We first employed the XGBClassifier algorithm (Chang et al., 2022) to train the model on the provided training dataset and evaluated its performance on a separate test set. The XGBoost-based approach achieved a macro F1… view at source ↗

**Figure 3.** Confusion matrix that shows results of BERT [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 2.** Confusion matrix that shows results of XGB [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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Recent Advances in Mathematical Research and Computer Science , volume=

Sentiment analysis: an approach for analysing tamil movie reviews using Tamil tweets , author=. Recent Advances in Mathematical Research and Computer Science , volume=

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A Preliminary Investigation into Sentiment Analysis of Informal Political Discourse. , author=. AAAI spring symposium: computational approaches to analyzing weblogs , pages=

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DLRG@ DravidianLangTech-ACL2022: Abusive comment detection in tamil using multilingual transformer models , author=. Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages , year=

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