A `Sourceful' Twist: Emoji Prediction Based on Sentiment, Hashtags and Application Source
This work addresses emoji prediction for social media users, but it is incremental as it builds on existing methods by adding new features.
The paper tackled the problem of emoji prediction in social media by incorporating underused Twitter features like hashtags and application source, achieving improved prediction accuracy through a neural network model.
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.