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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1, shrink-to-fit=no"
/>
<meta name="description" content="" />
<meta name="author" content="" />
<title>
CH-SIMS v2.0: A Fine-grained Multi-label Chinese Multimodal Sentiment
Analysis Dataset
</title>
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<li class="nav-item">
<a class="nav-link js-scroll-trigger" href="#Overview"
>Overview</a
>
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<a class="nav-link js-scroll-trigger" href="#down">Download</a>
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<!-- About -->
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<div class="container">
<div class="row">
<div class="col-lg-12">
<h2 class="section-heading text-center">
CH-SIMS v2.0: A Fine-grained Multi-label Chinese Multimodal
Sentiment Analysis Dataset
</h2>
<h3 id="Overview">Overview</h3>
<br />
<p>
<b>CH-SIMS v2.0</b>, a Fine-grained Multi-label Chinese Sentiment
Analysis Dataset, is an enhanced and extended version of CH-SIMS
Dataset. We re-labeled all instances in CH-SIMS to a finer
granularity and the video clips as well as pre-extracted features
are remade. We also extended the number of instances to a total of
14563. The new dataset contains videos collected from much wider
scenarios, as shown in the banner image.
</p>
<p>
As shown in below figure, CH-SIMS v2.0 contains
<b>4402 supervised instances</b>, denoted as CH-SIMS v2.0 (s), and
<b>10161 unsupervised instances</b>, denoted as CH-SIMS v2.0 (u).
The supervised instances share similar properties with CH-SIMS
dataset. The unsupervised instances show a much more diverse
distribution of video duration, which better simulates real-world
scenarios. The textual features of the unsupervised instances are
collected from the ASR transcript without manual correction and
thus contain noise, which also fits real-world scenarios better.
</p>
<div id="pic">
<img
src="./img/Total.png"
alt="Statistics"
width="900"
class="total information"
/>
</div>
<br />
<p>
We split train, valid and test set to a proportion of roughly
<b>9:2:3</b>. The regression labels range from -1 to 1. The
classification labels are: <b>Negative</b>(NEG),
<b>Weakly Negative</b>(WNEG), <b>Neutral</b>(NEU),
<b>Weakly Positive</b>(WPOS), and <b>Positive</b>(POS). The label
distribution is shown in below figure. The test set is
speaker-unrelated with the train/valid set.
</p>
<div id="pic">
<img
src="./img/datasplit.png"
alt="Data Split"
width="350"
class="data split"
/>
</div>
<br />
<p>
The <b>baseline experiments</b> are conducted via
<a href="https://github.com/thuiar/MMSA">MMSA platform</a>. The
results are reported <a href="https://github.com/thuiar/ch-sims-v2/tree/main#3-baselines-results">HERE</a> on Github.
</p>
</div>
</div>
</div>
</section>
<section>
<div class="container">
<div class="row">
<div class="col-lg-12">
<h3 id="down">Download</h3>
<br />
We provide raw video files as well as pre-extracted feature files.
Video IDs, splits and labels are inside the csv file.
<table border="0">
<tr>
<td><b>Dataset</b></td>
<td><b>Links</b></td>
</tr>
<tr>
<td>CH-SIMS v2.0 (s)</td>
<td>
<a
href="https://drive.google.com/drive/folders/1wFvGS0ebKRvT3q6Xolot-sDtCNfz7HRA?usp=sharing"
>
[Google Drive]
</a>
<a href=""> [Baiduyun Drive] </a>
</td>
</tr>
<tr>
<td>CH-SIMS v2.0 (u)</td>
<td>
<a
href="https://drive.google.com/drive/folders/1wFvGS0ebKRvT3q6Xolot-sDtCNfz7HRA?usp=sharing"
>
[Google Drive]
</a>
<a
href="https://pan.baidu.com/s/1845kp0sT5Dd91nO-WWZE6w?pwd=icmi"
>
[Baiduyun Drive]
</a>
</td>
</tr>
<tr>
<td>CH-SIMS</td>
<td>
<a href="https://drive.google.com/drive/folders/1oplJ15kdS_OK0wHXycI8p77jnmwAHhbG?usp=sharing"
>
[Google Drive]
</a>
<a href="https://pan.baidu.com/s/1wzz1TEFMGizWdPRBMh4Dbg?pwd=icmi"
>
[Baiduyun Drive]
</a>
</td>
</tr>
</table>
</div>
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</div>
</section>
<section>
<div class="container">
<div class="row">
<div class="col-lg-12">
<h3 id="pub">Citation</h3>
<br />
<p class="section-subheading text-muted">
Please cite us if you find our work useful.
</p>
<div class="ref">
<div class="authors">
[1] Yihe. Liu, Ziqi Yuan, Huisheng Mao, Zhiyun Liang, Wanqiuyue
Yang, Yuanzhe Qiu, Tie Cheng, Xiaoteng Li, Hua Xu, Kai Gao
</div>
<div class="title">
<a href="https://arxiv.org/abs/2209.02604">
Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and
AV-Mixup Consistent Module
</a>
</div>
<div class="links">
<a
onclick='if (document.getElementById("ref1").style.display=="none") document.getElementById("ref1").style.display="block"; else document.getElementById("ref1").style.display="none";'
>
Bibtex
</a>
| <a href="https://arxiv.org/pdf/2209.02604.pdf"> PDF </a>
</div>
<div style="display: block;" class="BibtexExpand" id="ref1">
<pre class="bibtex">
@misc{liu2022make,
title={Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module},
author={Yihe Liu and Ziqi Yuan and Huisheng Mao and Zhiyun Liang and Wanqiuyue Yang and Yuanzhe Qiu and Tie Cheng and Xiaoteng Li and Hua Xu and Kai Gao},
year={2022},
eprint={2209.02604},
archivePrefix={arXiv},
primaryClass={cs.MM}
}
</pre>
</div>
</div>
<br />
<div class="ref">
<div class="authors">
[2] Wenmeng Yu, Hua Xu, Fanyang Meng, Yilin Zhu, Yixiao Ma,
Jiele Wu, Jiyun Zou, Kaicheng Yang
</div>
<div class="title">
<a href="https://aclanthology.org/2020.acl-main.343/">
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with
Fine-grained Annotations of Modality
</a>
</div>
<div class="links">
<a
onclick='if (document.getElementById("ref2").style.display=="none") document.getElementById("ref2").style.display="block"; else document.getElementById("ref2").style.display="none";'
>
Bibtex
</a>
|
<a href="https://aclanthology.org/2020.acl-main.343.pdf">
PDF
</a>
</div>
<div style="display: none;" class="BibtexExpand" id="ref2">
<pre class="bibtex">
@inproceedings{yu2020ch,
title={Ch-sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality},
author={Yu, Wenmeng and Xu, Hua and Meng, Fanyang and Zhu, Yilin and Ma, Yixiao and Wu, Jiele and Zou, Jiyun and Yang, Kaicheng},
booktitle={Proceedings of the 58th annual meeting of the association for computational linguistics},
pages={3718--3727},
year={2020}
}
</pre>
</div>
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</section>
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