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A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods
arXiv - CS - Social and Information Networks Pub Date : 2021-06-16 , DOI: arxiv-2106.08829
Gullal S. Cheema, Sherzod Hakimov, Eric Müller-Budack, Ralph Ewerth

Opinion and sentiment analysis is a vital task to characterize subjective information in social media posts. In this paper, we present a comprehensive experimental evaluation and comparison with six state-of-the-art methods, from which we have re-implemented one of them. In addition, we investigate different textual and visual feature embeddings that cover different aspects of the content, as well as the recently introduced multimodal CLIP embeddings. Experimental results are presented for two different publicly available benchmark datasets of tweets and corresponding images. In contrast to the evaluation methodology of previous work, we introduce a reproducible and fair evaluation scheme to make results comparable. Finally, we conduct an error analysis to outline the limitations of the methods and possibilities for the future work.

中文翻译:

多模态推文情绪分析方法的公平和全面比较

意见和情感分析是表征社交媒体帖子中主观信息的一项重要任务。在本文中,我们对六种最先进的方法进行了全面的实验评估和比较,从中我们重新实现了其中一种方法。此外,我们研究了涵盖内容不同方面的不同文本和视觉特征嵌入,以及最近引入的多模式 CLIP 嵌入。针对推文和相应图像的两个不同的公开可用基准数据集提供了实验结果。与之前工作的评估方法相比,我们引入了可重复且公平的评估方案,以使结果具有可比性。最后,我们进行了错误分析,以概述方法的局限性和未来工作的可能性。
更新日期:2021-06-17
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