Classifying Tweets with Keras and TensorFlow using RNN (Bi-LSTM)

Abstract

Understanding public opinion, sentiment analysis, and subject recognition have all become more and more important as social media platforms have grown exponentially. The methodology for categorizing tweets using Keras and TensorFlow with a Recurrent Neural Network (RNN) architecture, specifically Long Short-Term Memory (LSTM) units, is presented in this research article. The method uses word embeddings and other properties to improve tweet representation, allowing the model to reliably identify specified categories and capture contextual connections. Our RNN-LSTM model beats baseline methods after extensive testing and evaluation, proving its suitability for tweet classification applications. The model's comprehension of tweet content is further improved by the incorporation of pre-trained word embeddings as well as features like emotion scores and hashtags. The approach offers a thorough framework for using deep learning methods in tweet classification, opening the door for uses cases including sentiment analysis, topic recognition, and opinion mining. By providing knowledge on the possibilities of RNN-LSTM models and their use in comprehending and analysing social media data, this research makes a contribution to the area. The results emphasise how crucial it is to take temporal dynamics and contextual factors into account while handling tweet classification jobs. Future research may concentrate on researching other pre-trained embeddings, investigating advanced RNN architectures, and solving issues with noisy and biassed twitter data. Overall, the large volume of information published on social networking sites like Twitter may now be better understood and analysed thanks to this research.