Deep learning models used for accuracy and loss detection on twitter dataset by setting tuning parameters

Abstract

Hate speech on social media post is running now a days. Social media like YouTube, Twitter, and Facebook etc. are responsible for hated speech. Hated speech spreads through digital media, causing individuals to get confused and adopt prejudiced viewpoints. To limit the negative effects of disinformation on the digital platform, it is critical to detect it. Now a days, lots of digital platforms are available. Hate speech detection in dataset is very difficult. As a result, the Twitter dataset is of the size of 25296 is presented in this work. Many deep learning techniques are applied on Twitter dataset. The Google Colab tool is used to scrape dataset material. Different deep learning approaches are utilized to boost the accuracy of the hated speech dataset. For training and validation accuracy and loss some models are used on Twitter dataset like Bi-directional Long Short Term Memory with Glove, Bi-LSTM, and Embedding from Language Model (Elmo) with deep learning, Convolutional Neural Network (CNN), Long Short Term Memory with Glove and LSTM. The performance of the proposed tweet dataset is evaluated using a variety of deep learning classifiers on text dataset. The planned deep learning techniques produced good results on tweet dataset. LSTM with Glove gave the highest accuracy 0.89 and minimum loss 0.19 on tweet dataset. So when compare our model on same dataset that was used earlier then we get highest accuracy and minimum loss.