Emotion-analysis

Project to develop rule-based and deep learning algorithms with an aim to first appropriately detect the different types of emotions contained in a collection of English sentences or a large paragraph and then accurately predict the overall emotion of the paragraph. Emotion analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics. The applications of emotion analysis are broad and powerful. The ability to extract insights from social data is a practice that is being widely adopted by organizations across the world. It can also be an essential part of your market research and customer service approach. Not only can you see what people think of your own products or services, you can see what they think about your competitors too. The overall customer experience of your users can be revealed quickly with emotion analysis, but it can get far more granular too. It can also be used to categorize feedback in movies, products, Hence, developing a tool for automating emotion Analysis can be very beneficial for various industries. I will approach the topic as a use case for exploratory data analysis, text mining and natural language processing (NLP) in social media