Active Learning for Customer Complaint Taxonomies: A Protocol for Complaint Text Triage, Sentiment-Aware Representation Learning, and Analytics-Ready Labeling

Smart World Journal Editorial Desk (Priya Nair, editorial pseudonym)

Abstract

Customer complaint taxonomies are often built under annotation scarcity, shifting service vocabularies, and uneven label granularity. This protocol defines an active learning workflow for complaint text triage that is designed for customer-service and review-oriented language, with a narrow scope: complaint intake, sentiment-aware representation learning, and integration into customer-analytics pipelines. The research question is whether an uncertainty-driven and domain-aware labeling process can reduce annotation waste while preserving taxonomy quality for downstream complaint routing, issue aggregation, and service monitoring. The proposed method combines a complaint-specific text encoder, sentiment markers, and a task router that selects items for annotation according to predictive uncertainty, class scarcity, and taxonomy conflict. A plain-text operational rule is specified for acquisition: select text x when score(x) = α·uncertainty(x) + β·rarity(x) + γ·conflict(x), with automatic routing permitted only when a complaint is detected and the top-label probability, confidence gap, and conflict thresholds are all satisfied. The protocol compares this active strategy with random sampling and static rule-based triage, using taxonomy consistency, route accuracy, calibration, label efficiency, and error concentration as primary outcomes. Prior work on customer-service chatbots, review sentiment transfer, and customer analytics pipelines supports the feasibility of using structured language representations and domain-aware pipeline construction, while structured prediction guidance supports matching method design to task structure. The manuscript does not report experiments; instead, it defines assumptions, decision steps, validation logic, and known failure conditions, including concept drift, nested complaints, and weak label definitions. The intended contribution is a precise, falsifiable protocol for building complaint taxonomies under limited annotation budgets.

Keywords: active learning; customer complaints; taxonomy construction; sentiment-aware representation; text triage; customer analytics; structured prediction

1. Introduction and Research Question

Hierarchical classification has been surveyed across application domains by Silla Jr. and Freitas. Their survey provides a relevant account of hierarchical prediction structures, rather than empirical validation of this complaint-routing proposal. The present protocol adopts a fixed parent-child taxonomy and evaluates its application to service complaints separately. Existing sentiment, chatbot and automated analytics studies motivate candidate representations, but do not establish the performance of their combination with the proposed active-learning queue. The design choices below are author-proposed and need independent application-specific testing. Hierarchy integrity is a schema requirement; it must not be confused with predictive accuracy or a measured improvement in service outcomes. [1] [2] [3]

2. Prior Evidence and Design Implications

A direct methodological implication of customer-service chatbot research is that service queries can be converted into structured representations before classification. In iDigiChat, customer queries are transformed into a graph format and then classified with a neural component. For the present protocol, this supports a staged triage design: first identify whether a message is a complaint, then estimate its most likely complaint family within the current taxonomy, and then send unresolved cases to human review under an explicit acquisition rule. [1]

Sentiment modeling research on customer reviews supplies a second design implication: complaint language often carries polarity shifts that are domain-specific. SentXFormer combines transformer embeddings, CNN-GRU sequence modeling, and adversarial adaptation to improve transfer across review domains, indicating that sentiment information should be represented explicitly rather than assumed to be separable from topic. For complaint taxonomies, this matters because negative polarity alone does not identify the complaint type, but polarity cues can improve discrimination between superficially similar categories such as service failure, delayed fulfillment, and product dissatisfaction. [4]

A third implication comes from customer-analytics pipeline research. Marketing-AutoM3L shows that generic automated ML systems may miss domain-specific customer features and that domain-aware feature engineering can include indicators such as recency-frequency-monetary measures, lifetime value, and engagement scores. A complaint taxonomy pipeline can therefore attach operational metadata where available, such as channel, timestamp, customer segment, queue, and service line. In this protocol, these variables are contextual covariates used jointly with text during routing and later pipeline integration; they are not treated as substitutes for complaint meaning. [5]

3. Proposed Task Definition and Variables

Let x_i denote the i-th service text and let y_i denote its taxonomy assignment. Two task forms are permitted, but one is the default. Default form: y_i is a single leaf label drawn from a finite set Y = {y_1, ..., y_K}, and each leaf has a unique parent in a fixed hierarchy. Sensitivity form: y_i is a set of one or more labels when the annotation guide explicitly permits multiple concurrent complaints. To keep acquisition and validation reproducible, the single-label hierarchical form is the operational default, and the multi-label form is used only in a prespecified sensitivity analysis with the same frozen taxonomy. The input may also include auxiliary variables z_i such as channel, product line, service date, customer segment, queue identifier, and prior contact count. These auxiliary variables are treated as contextual covariates, not as substitutes for the complaint text. [1] [5]

The sentiment-aware part of the representation is defined operationally as follows. For each complaint, a sentiment score s_i is extracted from the text encoder and attached as a feature vector component or attention signal. The score is not used as the label itself. Instead, it serves as a representation cue that may increase separation among complaint categories that share similar wording but differ in emotional stance or urgency. In this protocol, sentiment is therefore an auxiliary representation variable, not an outcome. [4] [2]

4. Active Learning Methodology

Operational rule: at iteration t, compute for every x in P_t the acquisition score A_t(x) = αu_t(x) + βr_t(x) + γc_t(x), where α, β, and γ are nonnegative weights fixed before deployment and normalized so that α + β + γ = 1. The default definitions are u_t(x) = 1 - max_y p_t(y|x,z), c_t(x) = 1 - m_t / E for an ensemble of size E with m_t equal to the number of ensemble members voting for the modal label, and r_t(x) = max_{y in S_k(x)} 1 / sqrt(n_t(y) + 1). Here S_k(x) is the set of the k most probable leaf labels under the current model, with default k = 2, and n_t(y) is the current labeled count for leaf y. Selection is one-shot from a frozen model state within each iteration: rank all items in P_t by descending A_t(x), choose the top b items as B_t, annotate them, update L_t, retrain or refresh M_t, and only then recompute scores for the next iteration. Ties are broken in this order: higher u_t(x), then higher r_t(x), then earlier arrival time, then random choice with a recorded seed. Automatic routing is governed by one explicit conjunction rule: route x automatically to the top leaf label if and only if the complaint detector is positive, max_y p_t(y|x,z) is at least τ_auto, gap_t(x) = p_(1)(x) - p_(2)(x) is at least δ_gap, c_t(x) is at most τ_conflict, and the full ancestor chain of the predicted leaf is available in the frozen taxonomy; otherwise do not auto-route. Default starting weights are α = 0.50, β = 0.30, and γ = 0.20. If these values are tuned, tuning must be preregistered on the development period only with a narrow grid, such as α in {0.4, 0.5, 0.6}, β in {0.2, 0.3, 0.4}, γ = 1 - α - β subject to γ greater than or equal to 0, and selection by a single prespecified criterion from the validation segment rather than post hoc adjustment. In the multi-label sensitivity analysis, u_t(x) is defined as the mean binary uncertainty across labels retained above a fixed candidate threshold, c_t(x) as one minus the mean inter-model Jaccard agreement over predicted label sets, and r_t(x) by the rarest candidate leaf among labels retained for x under that same threshold.

The representation layer is proposed as a sentiment-aware transformer encoder that is fine-tuned on complaint text and augmented with domain-adaptive masking. A second module encodes operational metadata z_i through a compact tabular encoder. The two streams are fused before classification. This design is intended to retain the contextual strength of transformer models while keeping the system usable in customer analytics settings where metadata often carries useful operational signal. The protocol does not require a specific pretrained model; the key requirement is that the encoder expose sentence-level embeddings and token-level attention that can support uncertainty estimation and review by annotators. [4] [5]

5. Validation Design and Outcome Measures

Validation should include ablation tests that remove sentiment features, metadata features, or the conflict term from the acquisition function. These ablations separate the proposed contribution of sentiment-aware representation from the contribution of active acquisition. The evaluation should also report stratified results for short complaints, long complaints, templated complaints, and complaints with mixed praise-and-criticism content. This stratification is justified prospectively because prior sentiment research shows that vocabulary, context, and sentiment expression vary across domains, and the same instability may affect complaint texts across service settings. [4]

6. Failure Cases, Boundary Conditions, and Limits of Generalization

The protocol has several identifiable failure cases. First, if the service taxonomy is poorly defined, active learning will amplify ambiguity rather than remove it. In that situation, the acquisition function may repeatedly request labels for texts that are unresolvable because the class definitions overlap. Second, if the customer service corpus contains highly templated text with limited semantic variety, uncertainty-based sampling may oversample near-duplicate cases and under-sample genuinely novel complaint forms. Third, if sentiment is used too aggressively, the learner may conflate emotional intensity with issue category and bias the taxonomy toward negative tone rather than operational content. [4] [2]

Schema, ensemble and evaluation controls

Validate the frozen taxonomy as acyclic and ancestor-complete before fitting. A missing ancestor is a schema error and defers routing; the system never invents an ancestor. The default conflict ensemble contains three independently seeded models. Their normalized vote disagreement is in [0,1]; if only one model is available, conflict is unavailable and its weight is removed before renormalizing the remaining nonnegative weights. Use k=min(2,K), and define the margin against zero when K=1. Rarity is bounded in [0,1], as is uncertainty, before combination. A proposed taxonomy-expansion trigger requires at least five independently recorded unresolved cases within 30 days from at least two users; the threshold is illustrative, not a validated adequacy criterion. Expansion creates a future version evaluated on new held-out cases. Primary performance is macro-F1 and rare-leaf recall at equal annotation cost; hierarchy consistency under a hard gate is a diagnostic, not evidence of learning improvement. Routing confidence and probability calibration require held-out evaluation, not inference from ensemble agreement alone.

7. Conclusion

Contribution and availability

Byline presentation updated 2026-11-03. The original credit was Roman Vlasov. Priya Nair is an editorial pseudonym, not an additional researcher. The study text, evidence and results are unchanged. Byline policy.

This manuscript proposes a bounded, falsifiable active learning protocol for customer complaint taxonomies that integrates complaint text triage, sentiment-aware representation learning, and customer-analytics metadata into a pool-based annotation workflow. Its specific contribution is a task-defined acquisition rule, a hierarchical routing design, and a validation plan tailored to complaint services rather than a general text-classification setting.

Funding: Roman Vlasov’s own funds. Editorial responsibility and competing interests: Roman Vlasov is the founder and Editor-in-Chief of Smart World Journal and the creator of the BILDRY construction-planning platform. Other competing interests have not been declared.

This conceptual study reports no newly collected empirical data. Its proposed procedures require the prospective validation described above.

References

He, Ji; Luo, Yi; Wang, Ting (2025). iDigiChat: intelligent digital marketing service chatbot for providing efficient customer services using artificial intelligence. Scientific Reports. 15(1), 33074. doi:10.1038/s41598-025-14722-5.
Mathew, Sujith Samuel; Hayawi, Kadhim; Venugopal, Neethu; El Barachi, May (2026). Quantifying customer sentiment for automobile brand perception analysis using machine learning on Twitter. Scientific Reports. 16(1), 5703. doi:10.1038/s41598-026-35637-9.
Silla, Carlos N.; Freitas, Alex A. (2011). A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery. 22(1-2), 31-72. doi:10.1007/s10618-010-0175-9.
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Tian, Ye; Shao, Wenqian; Deng, Zihan (2026). Marketing-AutoM3L: domain-aware automated machine learning for financial customer analytics. Frontiers in Artificial Intelligence. 9, 1726900. doi:10.3389/frai.2026.1726900.

[ref-1] He, Ji; Luo, Yi; Wang, Ting (2025). iDigiChat: intelligent digital marketing service chatbot for providing efficient customer services using artificial intelligence. Scientific Reports. 15(1), 33074. doi:10.1038/s41598-025-14722-5.

[ref-2] Mathew, Sujith Samuel; Hayawi, Kadhim; Venugopal, Neethu; El Barachi, May (2026). Quantifying customer sentiment for automobile brand perception analysis using machine learning on Twitter. Scientific Reports. 16(1), 5703. doi:10.1038/s41598-026-35637-9.

[ref-3] Silla, Carlos N.; Freitas, Alex A. (2011). A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery. 22(1-2), 31-72. doi:10.1007/s10618-010-0175-9.

[ref-4] Kumar, Ajeet; Abhishek, Kumar; Shafeeq B M, Ahamed (2025). SentXFormer: a transformer-enhanced hybrid deep learning framework for cross-domain sentiment analysis of customer reviews. Scientific Reports. 16(1), 3528. doi:10.1038/s41598-025-33526-1.

[ref-5] Tian, Ye; Shao, Wenqian; Deng, Zihan (2026). Marketing-AutoM3L: domain-aware automated machine learning for financial customer analytics. Frontiers in Artificial Intelligence. 9, 1726900. doi:10.3389/frai.2026.1726900.