We introduce a novel approach for generating 2D RGB color images with a plot from the micro text (tweet) to be used for the overall polarity classification process of sentiment analysis. Researchers generally use word embedding and external resource-based embedding techniques for text preprocessing of sentiment analysis through machine learning, neural networks, and natural language processing approaches. We sought to identify alternative ways to represent tweets for text classification. According to the experimental results, using the new ‘Text2Plot’ representation method could increase F1 scores by 27.2% for Convolutional neural networks (CNNs), 10.3% for support vector machine, and 4.4% for random forest models compared to using simple vectors as features for sentiment analysis. Hence, we propose this new method as a useful text representation approach for sentiment analysis, natural language processing tasks, and image processing problems. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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Text2Plot：通过创建文本的二维绘图表示来进行情感分析

我们介绍了一种新颖的方法，可使用微文本（推文）中的图生成2D RGB彩色图像，用于情感分析的整体极性分类过程。研究人员通常通过机器学习，神经网络和自然语言处理方法，使用词嵌入和基于外部资源的嵌入技术对情感分析进行文本预处理。我们试图找到替代方法来表示文本分类推文。根据实验结果，与使用简单矢量作为特征相比，使用新的“ Text2Plot”表示方法可以使卷积神经网络（CNN）的F1得分提高27.2％，支持向量机的F1得分提高10.3％，随机森林模型的F1得分提高4.4％。用于情感分析。因此，我们提出此新方法作为一种有用的文本表示方法，用于情感分析，自然语言处理任务和图像处理问题。©2021日本电气工程师学会。由Wiley Periodicals LLC发布。