---
title: "Beyond Polarity — paper by Meng et al. (2026)"
description: "The authors propose VADE, which adds continuous Valence–Arousal–Dominance (VAD) information to aspect-level prediction. 38 references."
authors: ["Ling-Ang Meng", "Tianyu Zhao", "Dawei Song", "Jingxu Cao", "Youhui Zuo"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1979/"
doi: "10.18653/v1/2026.findings-acl.1979"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/meng-2026-beyond-polarity-continuous-affect-enhanced/"
---
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# Beyond Polarity: Continuous Affect-Enhanced Multimodal Aspect-Based Sentiment Classification

**Authors:** Ling-Ang Meng, [Tianyu Zhao](https://md.research.elamur.ai/free/en/authors/tianyu-zhao/), [Dawei Song](https://md.research.elamur.ai/free/en/authors/dawei-song/), Jingxu Cao and Youhui Zuo · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1979/) · [PDF](https://aclanthology.org/2026.findings-acl.1979.pdf) · [DOI 10.18653/v1/2026.findings-acl.1979](https://doi.org/10.18653/v1/2026.findings-acl.1979) · [OpenAlex](https://openalex.org/W7166817726)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Most multimodal aspect-based sentiment classification (MABSC) systems treat sentiment as a discrete label and use generic image features. This makes them weak when the feeling toward an aspect in a tweet is mild, implicit, or carried mainly by the picture. The authors propose VADE, which adds continuous Valence–Arousal–Dominance (VAD) information to aspect-level prediction.

VADE has three parts:
- a BART encoder that reads the tweet together with the aspect term;
- a BERT-based regressor that produces a three-dimensional VAD vector, trained on weak sentence-level targets averaged from the NRC-VAD lexicon, which then modulates the text features;
- a CLIP image encoder made sensitive to emotion by fine-tuning on Senti-COCO, a new resource in which Qwen2.5-VL-7B rewrote MSCOCO captions to carry stronger affect.

The fused features feed an MLP classifier. VADE reaches state-of-the-art results on Twitter-15 and Twitter-17, and ablations show that every component contributes. The work matters because it brings dimensional affect modeling, previously mostly text-only and sentence-level, into fine-grained multimodal sentiment analysis.

## Key points
- **Senti-COCO:** about 123K affect-enriched image–caption pairs, built by prompting Qwen2.5-VL-7B to keep the visual content but make the captions more emotional, with a verification/filtering step. It is used for contrastive fine-tuning of CLIP. Code and data are at github.com/Maydayflower/VADE.
- **VAD encoder:** BERT plus a regression head, trained with MSE loss on pseudo-labels obtained by averaging NRC-VAD scores of matched words. NRC-VAD covers over 20,000 English words rated via Best-Worst Scaling.
- **Ablations:** removing Valence hurts performance most. Dropping Arousal or Dominance also reduces scores, which suggests these dimensions add complementary information.
- **Case study:** on example tweets, VADE gives more accurate aspect-level labels than TomBERT and VLP-MABSA, notably on metaphorical posts and on mildly worded neutral ones.
- **Limitations:** the paper covers only MABSC, not MATE or JMABSA. Rewritten captions may introduce stylistic bias or content drift. Lexicon-derived targets handle irony and compositional affect poorly.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Beijing Institute of Technology](https://md.research.elamur.ai/free/en/universities/beijing-institute-of-technology/) — University, Beijing, China · 34 works · 3,652 citations
- [The Open University](https://md.research.elamur.ai/free/en/universities/the-open-university/) — University, Milton Keynes, United Kingdom · 10 works · 338 citations

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## Cite

Cite as: Ling-Ang Meng, Tianyu Zhao, Dawei Song, Jingxu Cao and Youhui Zuo. 2026. Beyond Polarity: Continuous Affect-Enhanced Multimodal Aspect-Based Sentiment Classification. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{meng2026beyond,
  title = {{Beyond Polarity: Continuous Affect-Enhanced Multimodal Aspect-Based Sentiment Classification}},
  author = {Ling-Ang Meng, Tianyu Zhao, Dawei Song, Jingxu Cao and Youhui Zuo},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1979},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1979}
}
```

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*Updated 1 November 2026*