Sentiment Analysis on Video Transcripts: Comparing the Value of Textual and Multimodal Annotations

Quanqi Du; Loic De Langhe; Els Lefever; Veronique Hoste

doi:10.18653/v1/2025.wnut-1.2

Sentiment Analysis on Video Transcripts: Comparing the Value of Textual and Multimodal Annotations

Quanqi Du, Loic De Langhe, Els Lefever, Veronique Hoste

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Abstract

This study explores the differences between textual and multimodal sentiment annotations on videos and their impact on transcript-based sentiment modelling. Using the UniC and CH-SIMS datasets which are annotated at both the unimodal and multimodal level, we conducted a statistical analysis and sentiment modelling experiments. Results reveal significant differences between the two annotation types, with textual annotations yielding better performance in sentiment modelling and demonstrating superior generalization ability. These findings highlight the challenges of cross-modality generalization and provide insights for advancing sentiment analysis.

Anthology ID:: 2025.wnut-1.2
Volume:: Proceedings of the Tenth Workshop on Noisy and User-generated Text
Month:: May
Year:: 2025
Address:: Albuquerque, New Mexico, USA
Editors:: JinYeong Bak, Rob van der Goot, Hyeju Jang, Weerayut Buaphet, Alan Ramponi, Wei Xu, Alan Ritter
Venues:: WNUT | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 10–15
Language:
URL:: https://aclanthology.org/2025.wnut-1.2/
DOI:: 10.18653/v1/2025.wnut-1.2
Bibkey:
Cite (ACL):: Quanqi Du, Loic De Langhe, Els Lefever, and Veronique Hoste. 2025. Sentiment Analysis on Video Transcripts: Comparing the Value of Textual and Multimodal Annotations. In Proceedings of the Tenth Workshop on Noisy and User-generated Text, pages 10–15, Albuquerque, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):: Sentiment Analysis on Video Transcripts: Comparing the Value of Textual and Multimodal Annotations (Du et al., WNUT 2025)
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PDF:: https://aclanthology.org/2025.wnut-1.2.pdf

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@inproceedings{du-etal-2025-sentiment,
    title = "Sentiment Analysis on Video Transcripts: Comparing the Value of Textual and Multimodal Annotations",
    author = "Du, Quanqi  and
      De Langhe, Loic  and
      Lefever, Els  and
      Hoste, Veronique",
    editor = "Bak, JinYeong  and
      Goot, Rob van der  and
      Jang, Hyeju  and
      Buaphet, Weerayut  and
      Ramponi, Alan  and
      Xu, Wei  and
      Ritter, Alan",
    booktitle = "Proceedings of the Tenth Workshop on Noisy and User-generated Text",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.wnut-1.2/",
    doi = "10.18653/v1/2025.wnut-1.2",
    pages = "10--15",
    ISBN = "979-8-89176-232-9",
    abstract = "This study explores the differences between textual and multimodal sentiment annotations on videos and their impact on transcript-based sentiment modelling. Using the UniC and CH-SIMS datasets which are annotated at both the unimodal and multimodal level, we conducted a statistical analysis and sentiment modelling experiments. Results reveal significant differences between the two annotation types, with textual annotations yielding better performance in sentiment modelling and demonstrating superior generalization ability. These findings highlight the challenges of cross-modality generalization and provide insights for advancing sentiment analysis."
}

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%0 Conference Proceedings
%T Sentiment Analysis on Video Transcripts: Comparing the Value of Textual and Multimodal Annotations
%A Du, Quanqi
%A De Langhe, Loic
%A Lefever, Els
%A Hoste, Veronique
%Y Bak, JinYeong
%Y Goot, Rob van der
%Y Jang, Hyeju
%Y Buaphet, Weerayut
%Y Ramponi, Alan
%Y Xu, Wei
%Y Ritter, Alan
%S Proceedings of the Tenth Workshop on Noisy and User-generated Text
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, USA
%@ 979-8-89176-232-9
%F du-etal-2025-sentiment
%X This study explores the differences between textual and multimodal sentiment annotations on videos and their impact on transcript-based sentiment modelling. Using the UniC and CH-SIMS datasets which are annotated at both the unimodal and multimodal level, we conducted a statistical analysis and sentiment modelling experiments. Results reveal significant differences between the two annotation types, with textual annotations yielding better performance in sentiment modelling and demonstrating superior generalization ability. These findings highlight the challenges of cross-modality generalization and provide insights for advancing sentiment analysis.
%R 10.18653/v1/2025.wnut-1.2
%U https://aclanthology.org/2025.wnut-1.2/
%U https://doi.org/10.18653/v1/2025.wnut-1.2
%P 10-15

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Markdown (Informal)

[Sentiment Analysis on Video Transcripts: Comparing the Value of Textual and Multimodal Annotations](https://aclanthology.org/2025.wnut-1.2/) (Du et al., WNUT 2025)

Sentiment Analysis on Video Transcripts: Comparing the Value of Textual and Multimodal Annotations (Du et al., WNUT 2025)

ACL

Quanqi Du, Loic De Langhe, Els Lefever, and Veronique Hoste. 2025. Sentiment Analysis on Video Transcripts: Comparing the Value of Textual and Multimodal Annotations. In Proceedings of the Tenth Workshop on Noisy and User-generated Text, pages 10–15, Albuquerque, New Mexico, USA. Association for Computational Linguistics.