Multimodal Complaint Dataset Overview

Updated 25 November 2025

Multimodal complaint datasets are structured resources that combine text, audio, images, and video to capture complex user grievances across various domains.
They utilize advanced fusion methods and annotation schemes, leveraging deep learning models like CNNs, transformers, and attention mechanisms to classify and summarize complaints.
These datasets drive practical applications in customer service, regulatory compliance, and healthcare by facilitating robust, context-sensitive automated analyses.

A multimodal complaint dataset is a structured resource designed for the computational modeling and analysis of user complaints that span multiple data modalities, such as text, images, audio, and video. These datasets are integral to developing models capable of mining, classifying, summarizing, or generating complaints in diverse real-world contexts, including customer care, e-commerce, finance, and healthcare. Recent advances have expanded from unimodal text-based resources to corpora that integrate visual, acoustic, and contextual information, enabling the nuanced capture and automated understanding of complex user grievances.

1. Dataset Structures and Composition

Multimodal complaint datasets systematically align and annotate heterogeneous inputs, supporting both supervised learning and the development of evaluation protocols tailored to real-world complaint scenarios.

Notable datasets:

MM-MediConSummation: 467 anonymized psychiatrist–patient consultations annotated with multi-modal medical concern summaries, intent labels, patient demographics, recommendation summaries, and keywords. Composed of 74,473 utterances, ~11 minutes per session, with precise splits for training (80%), validation (6%), and testing (14%) (Tiwari et al., 2024).
ComVID: 1,175 short product-review complaint videos (mean 3.2 s), original review text, expert-written complaint descriptions, and emotion labels (dissatisfaction, frustration, disappointment, blame). Stratified into train/dev/test (940/118/117), with metadata on domain and aspect (Das et al., 24 Sep 2025).
CIViL: 2,004 multimodal dialogues (≈7,100 utterances), combining multi-turn customer–agent conversations and user-submitted images (screenshots, device photos). Focused on Apple-Support, annotated with fine-grained aspect (6-class ACD) and severity (4-class SD) labels (Singh et al., 18 Nov 2025).
MulComp: 433 publicly-accessible financial complaint videos, with 912 utterance-level 2s segments annotated for five financial aspects and complaint presence (3-class per aspect), supported by audio, Whisper transcripts, and frames at 3 fps (Das et al., 26 Feb 2025).

Dataset	Modalities	Annotation Unit
MM-MediConSummation	Text, video, audio, context	Turn / session
ComVID	Video, text, emotion	Video / description
CIViL	Text, image	Multi-turn dialogue
MulComp	Video, audio, text	Utterance segment

2. Modalities, Feature Representations, and Annotation Schemes

Multimodal complaint datasets encode and align features across heterogeneous data domains.

Text: Full or segmented transcripts, original review comments, and expert-annotated summaries or labels.
Video/Images: Frames extracted at specified fps (e.g., 10 fps, 3 fps), representation via deep CNN encoders (ResNet-152, ViT), key-frame selection (Katna, GMFlow), and per-segment visual aggregates.
Audio: Low-level acoustic features (pitch, MFCCs, voice statistics using openSMILE), Whisper transcription alignment.
Personal Context: Discrete patient age/gender (MM-MediConSummation), review/product metadata (ComVID), conversation domain constraints (CIViL: Apple-focused).
Annotation Schemes:
- Primary/secondary intent labels (MM-MediConSummation)
- Complaint aspects and severity scale (CIViL: 6 aspects × 4 severity)
- Aspect presence + complaint status (MulComp: per-segment, 5 aspects, 3-class)
- Emotion labels (ComVID: 4 classes)
- Key summaries, keywords, doctor's recommendations, focal video segments (MM-MediConSummation).

Inter-annotator reliability for these tasks is substantial (e.g., Fleiss’ κ: 0.64–0.81), indicating careful protocol design and expert review.

3. Formal Frameworks, Evaluation Metrics, and Baselines

Multimodal complaint resources both facilitate and require advanced model architectures and task-specific metrics.

Model Architectures

Contextualized Adapter Fusion (MM-MediConSummation): Transcript encoder hidden states $H \in \mathbb{R}^{l \times d}$ fused via learned gates with modality-conditioned keys/values, yielding $\hat{H}$ by attention-weighted sum across text, video, and personal context, supporting both intent recognition and summarization (Tiwari et al., 2024).
Retrieval-Augmented Generation (ComVID): VideoLLaMA2-7b with LoRA adapters, prepended by top- $k$ multimodal review context (indexed/retrieved via CLIP+FAISS), plus emotion embedding for complaint description generation (Das et al., 24 Sep 2025).
Meta-Fusion Expert Routing (VALOR, CIViL): Modular multi-expert framework: aspect and severity logits are fused with a semantic alignment score (cosine similarity of BERT/ViT embeddings), followed by classification via a 3-layer MLP adjusted by alignment (Singh et al., 18 Nov 2025).
CLIP-dual Encoder + ISEC (MulComp): Frozen CLIP encoders for text/video chunk-wise features, followed by multi-head self-attention and multimodal fusion. Segment-level outputs classified for each aspect via softmax, loss summed over all aspects (Das et al., 26 Feb 2025).

Metrics

Text Generation: BLEU, ROUGE-1/2/L, METEOR (MM-MediConSummation, ComVID).
Classification: Macro/micro F1, Hamming Loss, Precision/Recall (MulComp, CIViL).
Fluency/Readability: Perplexity, Coleman–Liau Index (ComVID).
Complaint Retention: $\mathrm{CR}_k = \frac{S_{\mathrm{NVader}, k} + ES_k + AS_k}{3}$ , aggregating sentiment, emotion, and aspect overlap for generated complaints (ComVID) (Das et al., 24 Sep 2025).

4. Domains, Applications, and Significance

Multimodal complaint datasets are representative of diverse domains:

Healthcare: Patient-doctor consultations for precision in intent recognition and patient concern summarization, critical for digital triage and mental-health resources (MM-MediConSummation) (Tiwari et al., 2024).
E-commerce/Product Reviews: Automated conversion of user-uploaded video evidence into clear complaints to improve customer service automation, accessibility for low-literacy Populations (ComVID) (Das et al., 24 Sep 2025).
Finance: Utterance-level aspect classification to inform regulatory compliance, helpdesk triage, and complaint trend analysis (MulComp) (Das et al., 26 Feb 2025).
Customer Support/Service Analytics: Fine-grained aspect and severity recognition in multi-turn customer dialogues, supporting escalation prediction and SDG-aligned product/service improvement (CIViL) (Singh et al., 18 Nov 2025).

These datasets enable automated, evidence-oriented complaint understanding, bridging human-centric service analysis with large-scale AI deployment.

5. Comparison with Legacy Corpora and Limitations

Multimodal complaint datasets offer a significant advance over unimodal or single-task datasets. For example:

DAIC-WOZ, Dr. Summarize, CoDEC, HOPE, GPT3-ENS SS: Each exhibits partial coverage (e.g., video+transcript, intent, or summaries alone), lacking the fusion of complaint-centric summaries, intent labels, personal context, and nonverbal cues present in MM-MediConSummation (Tiwari et al., 2024).
Class/Domain Skews: CIViL is domain-restricted (Apple Support), ComVID is heavily electronics-oriented, and MulComp is finance-exclusive, reflecting class imbalances and limiting generalizability (Singh et al., 18 Nov 2025, Das et al., 24 Sep 2025, Das et al., 26 Feb 2025).
Missing Modalities/Signals: Absence of audio transcripts in ComVID, no multi-image alignment in CIViL, and single-language focus in most datasets.
Scalability: Largest datasets remain under 10k samples; further scaling, linguistic diversification, and end-to-end multi-modal reasoning are noted as needed future directions (Das et al., 24 Sep 2025).

6. Impact, Future Directions, and Open Challenges

The release of multimodal complaint datasets has catalyzed research on robust, context-sensitive complaint understanding and summarization. Key impacts include:

Establishing benchmarks for cross-modal fusion, intent-extraction, aspect-based classification, and complaint summarization tasks.
Enabling practical deployments in customer care, healthcare consultation, and financial trend analytics.
Forming the foundation for models that align with domain-specific demands, such as complaint retention metrics and escalation triggers.

Open challenges remain in dataset scaling, multi-domain and multi-lingual transfer, enhanced emotion taxonomy, and integration of additional modalities (e.g., speech prosody). Semantic ambiguity, label subjectivity (especially in severity/emotion), and imbalance in aspect frequency continue to drive methodological innovations in dataset creation, annotation protocols, and model evaluation.

Relevant literature: (Tiwari et al., 2024, Das et al., 24 Sep 2025, Singh et al., 18 Nov 2025, Das et al., 26 Feb 2025).