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Emotion Detection and Sentiment Analysis of Static Images

机译:静态图像的情感检测与情感分析

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The usage of social media platform such as Facebook, Instagram, Flicker, etc. is rising day by day wherein images play a major role. It is said “An image is worth a thousand words”, people these days upload certain images on these sites to display their sentiments and emotions in the form of picture on almost every occasion. Images play the most important role in today's generation where it has become a major part of everyone's lives. Most of the prevailing research have focused on sentiment analyses of textual data, but only limited researches have focused on analyzing sentiment of visual data. In this project, we have explored the possibilities of Convolutional Neural Networks (CNN) to predict the various emotions (happiness, surprise, sadness, fear, anger and neutral) depicted by an image. These sort of predictions can be useful in applications for automatic tag predictions of the visual data available on social media platforms and understanding sentiments of the people and their emotions.
机译:社交媒体平台(如Facebook,Instagram,Flicker等)的用途是日益上升的一天,其中图像发挥着重要作用。据说“一张图像胜过千言万语”,人们这些天上传这些网站上的某些图像,以几乎每场次以图片的形式展示他们的情绪和情感。图像在今天的一代中发挥最重要的作用,在那里它已成为每个人的生命的主要部分。大多数现行研究都集中在文本数据的情绪分析中,但只有有限的研究专注于分析视觉数据的情绪。在这个项目中,我们探讨了卷积神经网络(CNN)的可能性,以预测图像描绘的各种情绪(幸福,惊喜,悲伤,恐惧,愤怒和中性)。这些预测可以在社交媒体平台上可用的视觉数据的自动标签预测的应用中有用,并且了解人们的情绪及其情绪。

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