Sentiment Analysis of Images using Machine Learning Techniques

Abstract

Sentiment analysis is the process of identifying the idea of a text. People share the comments on social media stating their knowledge of the event and would like to know if most other people had a good or bad experience at the same event. This distinction can be made through Emotional-Analysis. Sentiment analysis captures informal text comments, posts and images from all comments shared by different users and classifies comments into different categories as neutral, negative or positive. This is also called as polarity separation. Various different types of ML and in-depth learning methods may be utilised in Sentiment Analysis like Support Vector Machines, NB, Haar Cascade, LBPH, CNN, etc. Emerging rise in popularity in Social Media has established a trend of posting images in restaurants to express their opinion on the food, ambience, etc which can be a useful resource to obtain opinion and feedback from the Customers. In this paper, the implementation of Sentiment Analysis on images containing users along with their faces from the restaurants review revealing it more efficacious in classifying and identifying sentiments of review-images.

Keywords

• sentiment analysis
• product reviews
• image sentiment analysis
• lbph
• haar cascade