Visual Sentiment Analysis using Deep Learning Models: A Comparative Study
Payal Jain, Yasmin Shaikh, Sanjay Tanwani · Zenodo (CERN European Organization for Nuclear Research) · 2023
With the rapid development of social networking websites (SNWs), it has become a well established platform for communication and information exchange with the help of high speed Internet facilities have made it convenient for users to create and maintain their social profile account on online platform through which they can communicate with other users on the website. Analyzing the sentiments of social media user’s content through audio, text, image, and video plays an important role in many applications like human behaviour prediction, medical investigation, education mining, and market prediction, recommend system, fraud detection and many such applications. Sentiment analysis is used to predict the emotion or sentiment of user about their shared data. In current environment majority of people share images with substituting text as a common practice to show their feelings on the social networking websites like Facebook, Instagram, twitter as well as on any other platform. Analysis of Multimodal (image, text) data has been paid less attention as compared to single modality (image or text) data. Analysis of visual content is strenuous task in terms of detection and recognition of image data. For identification of emotions from visual content, image sentiment analysis techniques are used. It is evident from available literature that machine learning techniques have been widely used for image sentiment analysis. But the traditional feature extraction methods of machine learning had limited ability to produce accurate results. To overcome the limitation of traditional machine learning methods, deep learning models use automatic feature extraction method and produce more accurate results. Convolutional Neural Networks (CNN) provide state of the art model of deep learning in the field of image sentiment analysis. The aim of this work is to study different sentiment analysis techniques and as well as provide a comparative study of these technique.