Image Sentiment Classification Using Deep Learning Approach

Abstract

Image sentiment classification is very emerging trend due to high data generation in social media. In today's world, the proportion of individuals express their thoughts on the internet by replacing words with photo uploads on a wide range of social networking websites such as Instagram, FB, Twitter, as well as other platforms. Various visual elements along with image recognition strategies are applied to discern sentiments from image representation. Numerous previous systems have used machine learning (ML) methods to identify emotions, however typical extraction of features methodologies does not attain the requisite efficiency on different objects. In this paper we demonstrate the approach of image sentiment classification using deep learning technique. The training unit is responsible for image standardization, Feature extraction, classification, and selection throughout the procedure. This paper presents the most recent advancement in the area of picture sentiment using deep learning algorithms. We also examined the usage of traditional machine learning (ML) approaches against deep learning method. It appears that combining a rapid RNN (recurrent neural network) with a Convolutional Neural Network (CNN) can provide high precision while requiring minimal time complexities. According to a poll, present academics believe Convolutional Neural Network has an average precision of 96.50 percent for sentiment analysis on the flicker image corpora.