Emoji Recommendation System Using Deep Learning Algorithms

Emojis are becoming increasingly prevalent in everyday online communication such as messaging, email, and social networking. To enhance the user experience of expressing emotions and conveying information through emojis, various techniques have been developed. Our proposed system aims to analyze chat conversations, identify different emotions or topics of discussion, and suggest emojis that align with the context of the conversation. Our model considers contextual and personal information derived from the user's chat conversations to predict appropriate emojis. The emoji recommendation system is designed with several key considerations in mind. Firstly, our model considers entire conversations, rather than relying on individual sentences to predict emojis. To suggest suitable emojis based on the chat context, our emoji prediction system should take into account multiple previous messages. This includes not only the text and emojis exchanged between participants but also the identity of the speaker and the sequence of sentences within the chat. Secondly, our model seeks to capture different conversational contexts. Conversations can convey a broad range of emotions, information, and feelings. As such, the selection of emojis should be based on the specific context of the ongoing chat, and our recommendation model should offer users a range of emojis that align with different contexts.