阿拉伯语漫画中色彩与文字的情感关系

H. Jomaa, Mohamad Kamereddine, A. Nayal, Yara Rizk, M. Awad
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引用次数: 1

摘要

漫画书在许多国家都被视为一种文化遗产。在过去的十年中,随着数字化进程接管了印刷媒体,加上可用数据的丰富和多样化,对注释事件的彩色描述获得了越来越多的兴趣。在计算机视觉和自然语言处理领域已经设计了几个应用程序来处理漫画书页面。然而,在本文中,我们关注的是两者之间的联系,具体来说,我们比较了颜色和文字分别暗示的情感。色彩理论及其对人的情绪状态的影响可以追溯到19世纪。气球演讲中使用的语言也是为了吸引观众的注意力,操纵他们的情绪。在本文中,颜色理论被应用于分析页面的情感含义,并与minSVM的输出进行了比较,minSVM是一种改进的支持向量机分类器,它可以容纳不平衡的数据集,而常规支持向量机是在一个自定义数据库的提取文本上训练和实现的,以识别它们所传达的情感。使用minSVM,我们获得了91.26%的准确率,而使用SVM的准确率为89.66%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Affective Relationship between Color and Text in Arabic Comic Books
Comic books are considered a heritage in many countries. The colorful depiction of annotated events has gained an increasing amount of interest over the past decade as the digitization process took over printed media, in addition to the abundance and variety of available data. Several applications have been devised in the field of computer vision and natural language processing to handle comic book pages. However, in this paper, we focus on the connection between the two, specifically, we compare the emotions that color and text separately imply. The theory of color and its effect on one's emotional state can be dated back to the 1800's. The language used in speech balloons is also written to a way to capture the audience's attention and manipulate their emotions. Throughout this paper, the color theory is applied to analyze the pages' emotional implication, and compared to the output of minSVM, a modified SVM classifier that accommodates imbalanced datasets, and a regular SVM, that are trained and implemented on the extracted text of a homegrown database to identify the emotions they convey. Using minSVM, we obtained a 91.26 % accuracy as opposed to an 89.66 % with SVM.
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