emoji prediction machine learning

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Emojify: Emoji Prediction from Sentence

In this project we investigate the relation between words and emojis studying the novel task of predicting which emojis are evoked by text-based tweet messages We experimented variant of word embedding techniques and train several models based on Multinomial Naive Bayes and LSTMs in this task respectively

What is emoji prediction?
Emoji prediction is a fun variant of sentiment analysis. When texting your friends, emoji can make your text mes-sages more expressive. It would be nice if the keyboard can predict emojis based on the emotion and meaning of the whole sentence you typed out.
How to predict emojis using a hybrid algorithm?
This paper presents a novel method for emoji prediction using a hybrid algorithm by combining a deep learning model that uses the text, time, and location and two different algorithms which employ NLP-based techniques to map emojis to input text. The hybrid model consists of three channels, as shown in Fig. 1.
Can machine learning predict emojis?
Predicting emojis is more like a sentence classification problem. We first investigate the word embedding tech-niques to use, then try machine learning algorithms we learned in the class, such as Multinomial Naive Bayes, SVM and Bi-LSTM to do the emoji prediction.
How emojis are used in deep learning?
Emojis are a wonderful method to express oneself. This deep learning project automatically predicts emojis based on a given phrase. In this machine learning project, we predict the emoji from the given text. This means we build a text classifier that returns an emoji that suits the given text.

Emoji Prediction using LSTM Machine Learning Projects 3 Machine Learning Training Edureka

Live Emoji deep learning project #1 data collection source code

Emoji Prediction LSTM in Tensorflow Implementation

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Multi-class Sentiment Classification on Twitter using an Emoji - DiVA

emoji representations when multi-class sentiment analysis is performed Machine learning algorithms such as Naive Bayes classifiers have turned out to classifier can then be used to predict unseen samples with the most probable class,