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arxiv: 2605.03624 · v1 · submitted 2026-05-05 · 💻 cs.CL

Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model

Niklas Donhauser , Jakob Fehle , Nils Constantin Hellwig , Markus Weinberger , Udo Kruschwitz , Christian Wolff This is my paper

Pith reviewed 2026-05-07 04:14 UTC · model grok-4.3

classification 💻 cs.CL
keywords aspect-based sentiment analysisGerman ABSAannotation qualityinter-annotator agreementlarge language model annotationcrowdworker annotationsdataset constructionunder-resourced NLP
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The pith

Different sources of annotation for German aspect-based sentiment analysis produce datasets of varying quality that affect how well state-of-the-art models perform on fine-grained sentiment tasks.

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

The paper examines how the choice of who creates the labels influences the usefulness of datasets for aspect-based sentiment analysis in German. It re-annotates an existing dataset with experts to create a reference point and then measures how closely student, crowdworker, and large-language-model annotations match that reference, both in direct agreement and in the accuracy of models trained on each version. This comparison matters because high-quality labeled data remains scarce for languages other than English, so clearer information about reliability versus speed helps researchers decide how to allocate limited resources when building new datasets.

Core claim

Re-annotating an existing German ABSA dataset with experts creates a ground truth against which annotations from students, crowdworkers, and LLMs are compared using inter-annotator agreement and downstream performance on aspect category sentiment analysis and target aspect sentiment detection. State-of-the-art models based on BERT, T5, and LLaMA are trained and evaluated under both fine-tuning and in-context learning settings to quantify how annotation source changes task accuracy. The resulting measurements show clear differences in consistency and model effectiveness that point to concrete trade-offs between annotation reliability and the effort required to obtain it.

What carries the argument

Inter-annotator agreement metrics combined with controlled evaluation of model performance on ACSA and TASD subtasks after training on each annotation source.

If this is right

  • Models trained on expert annotations reach higher accuracy on German ACSA and TASD than models trained on the other sources.
  • Large language models can supply annotations at lower cost but introduce more variability that reduces final model performance.
  • Student and crowdworker annotations occupy an intermediate position in both agreement and downstream results.
  • Dataset builders for other under-resourced languages can use these measured trade-offs to choose annotation methods that match available time and budget.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Hybrid workflows that let LLMs generate initial labels and then route uncertain cases to experts could reduce total cost while preserving most of the quality gain.
  • The same source-comparison design could be applied to other sequence-labeling tasks such as named-entity recognition or opinion-target extraction in low-resource settings.
  • Performance differences observed on held-out test data may shrink or grow when the models are tested on entirely new German text from different domains.

Load-bearing premise

That the expert re-annotations form an unbiased and stable reference that fairly represents the correct labels for the texts.

What would settle it

A second independent group of experts re-annotating the same texts and producing labels that agree more with student or LLM annotations than with the first expert set would undermine the chosen ground truth.

Figures

Figures reproduced from arXiv: 2605.03624 by Christian Wolff, Jakob Fehle, Markus Weinberger, Niklas Donhauser, Nils Constantin Hellwig, Udo Kruschwitz.

Figure 1
Figure 1. Figure 1: Example annotations of the same text by four groups (crowdworkers, students, LLMs, and view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of aspect categories across the five datasets for the TASD and ACSA tasks, reported view at source ↗
Figure 3
Figure 3. Figure 3: Label interfaces used for the ACSA and TASD annotation tasks. While not shown in the view at source ↗
read the original abstract

Aspect-Based Sentiment Analysis (ABSA) enables fine-grained opinion analysis by identifying sentiments toward specific aspects or targets within a text. While ABSA has been widely studied for English, research on other languages such as German remains limited, largely due to the lack of high-quality annotated datasets. This paper examines how different annotation sources influence the development of German ABSA. To this end, an existing dataset is re-annotated by experts to establish a ground truth, which serves as a reference for evaluating annotations produced by students, crowdworkers, Large Language Models (LLMs), and experts. Annotation quality is compared using Inter-Annotator Agreement (IAA) and its impact on downstream model performance for different ABSA subtasks. The evaluation focuses on Aspect Category Sentiment Analysis (ACSA) and Target Aspect Sentiment Detection (TASD). We apply State-of-the-Art (SOTA) methods for ABSA, including BERT-, T5-, and LLaMA-based approaches to assess performance differences, spanning fine-tuning and in-context learning with instruction prompts. The findings provide practical insights into trade-offs between annotation reliability and efficiency, offering guidance for dataset construction in under-resourced Natural Language Processing (NLP) scenarios.

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

1 major / 1 minor

Summary. The paper claims that re-annotating an existing German ABSA dataset with experts establishes a reliable ground truth against which student, crowdworker, and LLM annotations can be compared via IAA and downstream performance on ACSA and TASD subtasks; SOTA models (BERT, T5, LLaMA) in fine-tuning and in-context learning settings then reveal practical reliability-efficiency trade-offs for dataset construction in under-resourced NLP.

Significance. If the expert ground truth is validated, the work supplies concrete guidance for low-resource ABSA dataset creation by quantifying when cheaper sources (LLMs, crowdworkers) can substitute for experts without substantial downstream loss. The dual IAA-plus-task-performance evaluation is a strength that could inform annotation protocols beyond German ABSA.

major comments (1)
  1. [Abstract and §3] Abstract and §3 (Annotation and Evaluation Setup): The central comparisons rest on expert re-annotations as the reference standard, yet no IAA among experts, disagreement-resolution protocol, or comparison to the original dataset labels is reported. Without these metrics the observed gaps for students, crowdworkers, and LLMs cannot be confidently attributed to quality rather than reference bias or task-interpretation differences; this assumption is load-bearing for all trade-off claims.
minor comments (1)
  1. [Abstract] The abstract mentions 'SOTA methods' and 'instruction prompts' without naming the exact model variants, prompt templates, or hyper-parameters used in the in-context learning experiments; this reduces reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights an important aspect of our methodology. We address the major comment below and will incorporate revisions to strengthen the validation of our expert ground truth.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Annotation and Evaluation Setup): The central comparisons rest on expert re-annotations as the reference standard, yet no IAA among experts, disagreement-resolution protocol, or comparison to the original dataset labels is reported. Without these metrics the observed gaps for students, crowdworkers, and LLMs cannot be confidently attributed to quality rather than reference bias or task-interpretation differences; this assumption is load-bearing for all trade-off claims.

    Authors: We agree that these details are necessary to rigorously establish the expert re-annotations as a reliable reference standard and to rule out reference bias. In the revised manuscript, we will expand §3 to report inter-annotator agreement among the expert annotators (using metrics such as Fleiss' kappa for multi-label aspects and sentiments), describe the disagreement-resolution protocol (e.g., discussion rounds leading to consensus), and include a direct comparison of the expert re-annotations against the original dataset labels, quantifying differences in aspect coverage and sentiment assignments. These additions will allow readers to assess whether performance gaps for students, crowdworkers, and LLMs stem from annotation quality rather than inconsistencies in the ground truth, thereby reinforcing the validity of our reliability-efficiency trade-off claims for ACSA and TASD. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical comparison anchored to expert reference without self-referential derivations

full rationale

The paper presents a purely empirical study that re-annotates an existing German ABSA dataset with experts to create a reference, then measures IAA and downstream ACSA/TASD model performance for student, crowdworker, and LLM annotations against that reference. No equations, fitted parameters, or predictive derivations appear in the described method; the evaluation pipeline does not reduce any output to its inputs by construction. Standard IAA metrics and fine-tuned/in-context model runs constitute independent benchmarks rather than tautological restatements. No load-bearing self-citations, uniqueness theorems, or ansatzes are invoked to justify the core claims. The design is self-contained against the external expert annotations as reference, which is a conventional methodological choice in annotation-quality research and does not create circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that expert annotations can serve as ground truth; no free parameters or invented entities are introduced in the abstract description.

axioms (1)
  • domain assumption Expert annotations of the existing dataset provide a reliable ground truth for ABSA.
    Used as the reference standard for evaluating all other annotation sources.

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