Image Sentiment Analysis Using Deep Learning

Abstract

Sentiments are feelings, emotions likes and dislikes or opinions which can be articulate through text, images or videos. Sentiment Analysis on web data is now becoming a budding research area of social analytics. Users express their sentiments on the web by exchanging texts and uploading images through a variety of social media like Instagram, Facebook, Twitter, WhatsApp etc. A lot of research work has been done for sentiment analysis of textual data; there has been limited work that focuses on analyzing the sentiment of image data. Image sentiment concepts are ANPs i.e. Adjective Noun Pairs automatically discovered tags of web images which are useful for detecting the emotions or sentiments conveyed by the image. The major challenge is to predict or identify the sentiments of unlabelled images. To overcome this challenge deep learning techniques are used for sentiment analysis, as deep learning models have the capability for effectively learning the image behavior or polarity. Image recognition, image prediction, image sentiment analysis, and image classification are some of the fields where Neural Network (NN) has performed well implying significant performance of deep learning in image sentiment analysis. This paper focuses on some of the noteworthy models of deep learning as Deep Neural Network (DNN), Convolutional Neural Network (CNN), Region-based CNN (R-CNN) and Fast R-CNN along with the suitability of their applications in image sentiment analysis and their limitations. The study also discusses the challenges and perspectives of this rising field.