TY  - JOUR
AU  - Raj, Helen Wilfred 
AU  - Balachandran, Santhi 
PY  - 2019
TI  - Future Emoji Entry Prediction Using Neural Networks
JF  - Journal of Computer Science
VL  - 16
IS  - 2
DO  - 10.3844/jcssp.2020.150.157
UR  - https://thescipub.com/abstract/jcssp.2020.150.157
AB  - In today’s world, textual data has made momentous progress in social media. The rise of digital communication via text has paved the way to emoji, a pictographically represented way of expressing emotions. In digital communication, Emoji gives a visual appeal to the text, which improves communication and new vistas of exchange and creativity. While emoji entry prediction based on text is well optimized, based on the neural network model, predicting the future emojis from images is not so easy due to lack of knowledge on the same. While effective models already exist for generating text descriptions of images, less attention has been given to models of symbolic description. We have used two models for predicting emoji from images, convolutional neural network architecture for image classification and an emoji2vec embedding into word2vec model. We have also done a sentiment analysis of the text for predicting future emoji labels. Our model captures the relation between emojis in an optimized way. This model has optimized the search time for future emoji entry predictions from images.