To identify sentiments and emotions of people towards a certain goal, in order to accomplish that we use sentiment analysis. Emotion detection is a subset of sentiment analysis as it focuses on a unique emotion behind the text rather than classifying it as a positive, negative or neutral statement. This study investigates the application of Natural Language Processing (NLP) alongside a combined method that integrates machine learning (ML) and deep learning (DL) techniques for emotion recognition from text. We analyse several ML and DL models, suggest a hybrid method that utilises their advantages, and assess their performance against standard datasets. The findings of previous research reveal that the hybrid method significantly enhances accuracy in emotion detection. Furthermore, we highlight areas that require additional research and recommend topics for future studies.

Emotion Detection, Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), Hybrid Approach, Sentiment Analysis.

This article highlights the possible implications of hybrid methods to help the field of emotion detection. By thoughtfully combining machine learning and deep learning techniques, it is possible to build systems that better and more reliably detect emotion in text. There is still a long way to go, and it is worth noting that while we have made good progress, there is much to be done in research to overcome some of the existing limitations, especially in the cases of detecting subtle emotions and generalising across a variety of text contexts. Robust, contextually-aware, adaptable emotion detection systems will lead to many valuable applications that will enhance human-computer interaction, improve communication, and provide a valuable understanding of human emotion.

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