MovieEmotion-IMG: An Emotion Distribution Dataset of Movie Scene Images

Jingjing Zhang, Chen Lin, Chunxiao Wang, Yicong Dong, Wei Jiang
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Abstract

One of the biggest challenges in sentiment analysis is to predict the complex feelings that humans evoke when viewing images. Due to the limitation of the quality and quantity of the dataset, the current learning-based algorithm mainly analyzes a single dominant emotion. Based on the above, this paper introduces an emotion distribution dataset of movie scene images with rich emotional semantics and professional aesthetic design, called MovieEmotion-IMG. Firstly nine labels, including eight emotional words and a “neutrality” label, were identified, and a set of emotional evaluation rules were designed. Then, the emotion distribution annotation experiment was carried out on 17140 movie images, and an emotion annotation system was developed to assist the annotation work. Finally, two reliability test methods were designed based on the external reliability test method to verify the reliability of the results and ensure the availability of the dataset. This is the first large-scale emotion dataset of movie scene images and annotated distributed emotions to our knowledge.
MovieEmotion-IMG:电影场景图像的情感分布数据集
情感分析的最大挑战之一是预测人类在观看图像时所唤起的复杂情感。由于数据集质量和数量的限制,目前基于学习的算法主要分析单一的主导情绪。在此基础上,本文引入了一个具有丰富情感语义和专业美学设计的电影场景图像情感分布数据集MovieEmotion-IMG。首先识别出包括8个情绪词和一个“中立”标签在内的9个标签,并设计了一套情绪评价规则。然后,对17140幅电影图像进行了情感分布标注实验,并开发了情感标注系统来辅助标注工作。最后,在外部信度测试方法的基础上,设计了两种信度测试方法,验证结果的可靠性,保证数据集的可用性。这是我们所知的第一个大规模的电影场景图像情感数据集,并标注了分布式情感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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