Stickers are increasingly used in social media to express sentiment and intent. When finding typing troublesome, people often use a sticker instead. Despite the significant impact of stickers on sentiment analysis and intent recognition, little research has been conducted. To address this gap, we propose a new task: Multimodal chat Sentiment Analysis and Intent Recognition involving Stickers (MSAIRS). Additionally, we introduce a novel multimodal dataset containing Chinese chat records and stickers excerpted from several mainstream social media platforms. Our dataset includes paired data with the same text but different stickers, and various stickers consisting of the same images with different texts, allowing us to better understand the impact of stickers on chat sentiment and intent. We also propose an effective multimodal joint model, MMSAIR, for our task, which is validated on our datasets and indicates that visual information of stickers counts. Our dataset and code will be publicly available.

翻译：贴纸在社交媒体中被广泛用于表达情感和意图。当用户觉得打字麻烦时，常会选择使用贴纸。尽管贴纸对情感分析和意图识别具有显著影响，但目前相关研究仍较为缺乏。针对这一空白，我们提出了一项新任务：涉及贴纸的多模态聊天情感分析与意图识别（MSAIRS）。此外，我们引入了一个新颖的多模态数据集，该数据集包含从多个主流社交媒体平台摘录的中文聊天记录与贴纸。我们的数据集包含相同文本搭配不同贴纸的配对数据，以及相同图像搭配不同文本的各类贴纸，从而能够更深入地理解贴纸对聊天情感与意图的影响。同时，我们针对该任务提出了一种有效的多模态联合模型MMSAIR，并在数据集上验证了其有效性，结果表明贴纸的视觉信息至关重要。我们的数据集和代码将公开发布。