  We widely use emojis in social networking to heighten, mitigate or negate the
sentiment of the text. Emoji suggestions already exist in many cross-platform
applications but an emoji is predicted solely based a few prominent words
instead of understanding the subject and substance of the text. Through this
paper, we showcase the importance of using Twitter features to help the model
understand the sentiment involved and hence to predict the most suitable emoji
for the text. Hashtags and Application Sources like Android, etc. are two
features which we found to be important yet underused in emoji prediction and
Twitter sentiment analysis on the whole. To approach this shortcoming and to
further understand emoji behavioral patterns, we propose a more balanced
dataset by crawling additional Twitter data, including timestamp, hashtags, and
application source acting as additional attributes to the tweet. Our data
analysis and neural network model performance evaluations depict that using
hashtags and application sources as features allows to encode different
information and is effective in emoji prediction.
