Visual Sentiment Analysis Using Machine Learning for Entertainment Applications
Anu Sayal, N Chaithra, Janhvi Jha, Balu Trilochan, G. Kalyan, Madhavan Priya, Veethika Gupta, Minakshi Memoria, Ashulekha Gupta
Abstract
The expression of emotions through images is said to be much more effective than via text. Therefore, it is important to develop an image-based model for sentiment analysis. The objective of visual sentiment analysis is to determine how various types of images affect viewers' emotions. Despite the fact that this topic is still relatively young, numerous strategies have been created for a variety of data sources and issues, leading to a substantial body of study. During sentiment analysis, feelings are sorted into optimistic, unpleasant, and unbiased categories. With the aid of machine learning, this work provides an analysis of the most recent advancement, theoretical and practical ideas defining an improvement in sentiment analysis for entertainment applications, specifically on social media. This work aims to focus at latest studies in emoticon-based sentiment analysis and machine learning for review images.