Comparative Analysis of Emotion Classification using TF-IDF Vector

In the current digital era, it is essential to effectively communicate in online settings, and emojis have emerged as a key tool for doing so. In order to improve the precision and usability of emoji selection within digital communication systems, this study offers a novel alternative. The creation of an advanced emotion classification system, supported by a semantic search algorithm, forms the basis of the suggested system. The solution provides a seamless and context-aware experience, in contrast to conventional methods that rely on manual emoji selection or imprecise keyword-based emotion detection. The system surpasses the restrictions of keyword matching by effectively identifying the emotional content within phrases or paragraphs by utilizing cutting-edge natural language processing techniques. Emoji suggestions are given to users by the system along with a Random Forest-based emotion categorization algorithm and real-time emoji-Emotion mapping, redefining how emotions are communicated in digital chats.