[Paper] Enhancing Visual Sentiment Analysis via Semiotic Isotopy-Guided Dataset Construction

Source: arXiv - 2512.14665v1

Overview

Visual Sentiment Analysis (VSA) tries to teach machines to “feel” what an image conveys—whether it looks happy, sad, nostalgic, or unsettling. The new paper proposes a systematic way to build much larger, more emotionally diverse image datasets by leveraging the concept of semiotic isotopy. The authors show that models trained on these enriched datasets generalize far better across standard VSA benchmarks, opening the door to more reliable emotion‑aware applications.

Key Contributions

• Semiotic‑Isotopy‑Guided Dataset Construction – a novel pipeline that expands existing image collections while preserving and diversifying their emotional semantics.
• Emotion‑Focused Annotation Strategy – introduces a lightweight, semi‑automatic labeling scheme that highlights emotionally salient image elements (objects, colors, composition).
• Cross‑Dataset Generalization Boost – empirical evidence that models trained on the isotopy‑augmented dataset outperform those trained on the original sources on all major VSA testbeds.
• Open‑Source Toolkit – the authors release code and a ready‑to‑use, 1.2 M‑image dataset, enabling immediate experimentation.

Methodology

1. Seed Collections – Start with several public VSA datasets (e.g., FlickrSentiment, TwitterEmotion).
2. Semiotic Isotopy Extraction – Treat each image as a semiotic system (a set of signs: objects, colors, layout). Using a combination of pre‑trained object detectors, color histograms, and scene classifiers, the pipeline extracts a compact “semantic signature.”
3. Isotopic Transformation – Apply controlled transformations (style transfer, background substitution, object insertion/removal) that preserve the original emotional signature while generating visually distinct variants.
4. Emotion Consistency Filtering – A lightweight sentiment classifier (trained on the seed data) scores each synthetic image; only those whose predicted sentiment matches the seed’s label are kept.
5. Human‑in‑the‑Loop Validation – A small crowd‑sourced verification step (≈5 % of the generated set) ensures that the isotopic transformations did not unintentionally flip the emotion.

The result is a balanced, high‑variance dataset where each emotional class is represented by thousands of isotopically related images, encouraging models to learn what drives sentiment rather than memorizing superficial cues.

Results & Findings

• Consistent Gains: Across six public VSA benchmarks, the isotopy‑trained models improve accuracy by 8–12 % points.
• Robust Feature Learning: Visualization of attention maps shows the models focus on semantically meaningful regions (e.g., smiling faces, warm lighting) rather than dataset‑specific artifacts.
• Data Efficiency: Even when training with only 30 % of the augmented data, performance matches or exceeds models trained on the full original collections.

Practical Implications

• Emotion‑Aware UI/UX: Apps that adapt theme colors, music, or content recommendations based on user‑generated images can now rely on more reliable sentiment predictions.
• Social Media Monitoring: Brands can detect shifts in public mood with higher confidence, reducing false positives caused by dataset bias.
• Creative Tools: Photo‑editing software can suggest filters or compositions that amplify a desired emotional tone, powered by models trained on isotopically diverse examples.
• Cross‑Domain Deployment: Because the models generalize better, developers can ship a single VSA engine to multiple platforms (mobile, web, AR) without extensive re‑training.

The open‑source toolkit also means teams can quickly generate domain‑specific sentiment datasets (e.g., medical imaging, advertising) by feeding in their own seed images.

Limitations & Future Work

• Semiotic Definition Scope: The current isotopy formulation focuses on objects, colors, and layout; more abstract cues (facial expressions, cultural symbols) are not fully captured.
• Computational Cost: Generating the full 1.2 M‑image dataset requires GPU‑accelerated style transfer and detection pipelines, which may be prohibitive for small labs.
• Human Validation Ratio: While only 5 % of images are manually checked, scaling to niche domains might need higher verification to avoid subtle sentiment drift.

Future research directions include extending isotopy to temporal media (video sentiment), incorporating multimodal cues (text + image), and exploring self‑supervised pre‑training that directly leverages the isotopic relationships.

Bottom line: By marrying semiotic theory with modern data‑augmentation pipelines, this work delivers a practical, high‑impact boost to visual sentiment analysis—making emotion‑aware AI more reliable and ready for real‑world deployment.

Authors

• Marco Blanchini
• Giovanna Maria Dimitri
• Benedetta Tondi
• Tarcisio Lancioni
• Mauro Barni

Paper Information

• arXiv ID: 2512.14665v1
• Categories: cs.CV
• Published: December 16, 2025
• PDF: Download PDF