Emotion detection from text using recurrent neural network Available to Purchase

Artificial Intelligence has been used to interpret data to make decisions and understand the emotions of humans. Sentiment analysis is a technique for determining people’s attitudes, feelings, and emotions about a certain purpose, such as people, or organizations. Since the emergence of the internet, individuals have used text messaging as a source to communicate with each other. As a result, in today’s virtual community, robots must interpret emotions in views, comments, and textual exchanges to give emotionally aware replies to users. Emotion detection is a subtype of sentiment analysis that predicts a specific emotion rather than just reporting good, negative, or neutral. Many researchers have previously worked on voice and facial expressions for emotion identification in recent years. However, detecting emotions in the text is a time-consuming operation. Humans easily recognize emotions, but the real issue emerges with technology. To discern emotions in text, machines require an accurate algorithm. This study uses a data set from open-source Kaggle. Our algorithm, unlike previous research, can detect emotions from text with an at most accuracy of over 86%.