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.
翻译:最近因特网和社交媒体的技术进步导致通信平台发展得更快、效率更高,这些平台包括视觉、文字和语言媒体,带来了一种独特的社会现象,称为因特网迷因。互联网迷因的形式是带有机智、可捕捉或讽刺文字描述的图像。在本文中,我们展示了一种多模式情绪分析系统,使用深层神经网络,将计算机视野和自然语言处理结合起来。我们的目的不同于正常的情绪分析目标,即预测文本表达积极或消极情绪;相反,我们的目标是将互联网迷因归类为积极、消极或中性,确定表达的幽默类型,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,并超过了基线分数。