论文标题
Gundapusunil在Semeval-2020任务8：多模式备忘录分析
gundapusunil at SemEval-2020 Task 8: Multimodal Memotion Analysis
论文作者
论文摘要
互联网和社交媒体使用方面的最新技术进步导致了更快，高效的沟通平台的发展。这些平台包括视觉，文本和语音媒介，并带来了一种称为互联网模因的独特社会现象。互联网模因以机智，吸引人或讽刺的文本描述的形式形式。在本文中，我们使用将计算机视觉和自然语言处理结合的深神经网络提出了多模式情绪分析系统。我们的目标与预测文本表示积极或负面情绪的正常情感分析目标不同。取而代之的是，我们旨在将互联网模因分类为一个积极的，消极或中立的，确定表达的幽默类型并量化表达特定效果的程度。我们的系统是使用CNN和LSTM开发的，并表现优于基线得分。
Recent technological advancements in the Internet and Social media usage have resulted in the evolution of faster and efficient platforms of communication. These platforms include visual, textual and speech mediums and have brought a unique social phenomenon called Internet memes. Internet memes are in the form of images with witty, catchy, or sarcastic text descriptions. In this paper, we present a multi-modal sentiment analysis system using deep neural networks combining Computer Vision and Natural Language Processing. Our aim is different than the normal sentiment analysis goal of predicting whether a text expresses positive or negative sentiment; instead, we aim to classify the Internet meme as a positive, negative, or neutral, identify the type of humor expressed and quantify the extent to which a particular effect is being expressed. Our system has been developed using CNN and LSTM and outperformed the baseline score.
