Visual content, such as photographs and video, contains not only objects, locations, and events, but also emotional and sentimental clues. On social networking sites, images are the simplest way for people to communicate their emotions. Images and videos are increasingly being used by social media users to express their ideas and share their experiences. Sentiment analysis of such large-scale visual content can aid in better extracting user sentiments toward events or themes, such as those in image tweets, so that sentiment prediction from visual content can be used in conjunction with sentiment analysis of written content. Despite the fact that this topic is relatively new, a wide range of strategies for various data sources and challenges have been created, resulting in a substantial body of study. This paper introduces the area of Image Sentiment Analysis and examines the issues that it raises. A description of new obstacles is also included, as well as an assessment of progress toward more sophisticated systems and related practical applications, as well as a summary of the studys findings.