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Emoji Prediction from Twitter Data using Deep Learning Approach

2021-10-04 · Asian Conference on Innovation in Technology (ASIANCON) 2021 10 · V.N.Durga Pavithra Kollipara, V.N.Hemanth Kollipara; M.Durga Prakash

Emojis are a small visual representation of emotions or objects that are usually used in text messages to enhance the communication experience between individuals. With the rise in the widespread use of social media platforms like Twitter and instant messaging, many users are using these emojis in their text messages to convey broad feelings efficiently, which sometimes cannot be expressed using just words. This combination of text and emojis to improve emotion has become an essential part of how people communicate in the 21st century. Thus, giving rise to a problem statement that is to identify the relationship between these text messages and the emojis used in them. In this paper, we propose an approach to predict multiple emojis for a given text-based tweet message. Our proposal contains three modules, where the first module preprocesses the given text data, the second module is the model on which the data is trained, and a multi-class classifier to predict the emojis evoked by the given text. The objective of this model is to understand the underlying semantics of the text sentence using natural language processing techniques to predict reasonable emojis.

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Code (1)

Defcon27/Emoji-Prediction-using-Deep-Learning 공식 구현

Tasks

Deep LearningMulti-class ClassificationPredictionSentenceText Classification

Methods 이 논문이 사용한 방법론

Tanh Activation 설명 없음
Sigmoid Activation 설명 없음
SVM A Support Vector Machine, or SVM, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs…
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…
BiLSTM A Bidirectional LSTM, or biLSTM, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards…

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