Comparing LSTM and CNN Methods in Case Study on Public Discussion about Covid-19 in Twitter

Abstract

This study compares two Deep Learning model methods, which include the Long Short-Term Memory (LSTM) method and the Convolution Neural Network (CNN) method. The aim of the comparison is to discover the performance of two different fundamental deep learning approaches which are based on convolutional theory (CNN) and deal with the vanishing gradient problem (LSTM). The purpose of this study is to compare the accuracy of the two methods using a dataset of 4169 obtained by crawling social media using the Twitter API. The Tweets data we've obtained are based on a specific hashtag keyword, namely "covid-19 pandemic". This study attempts to assess the sentiment of all tweets about the Covid-19 viral epidemic to determine whether tweets about Covid-19 contain positive or negative thoughts. Before classification, the Preprocessing and Word Embedding steps are completed, and this study has determined that the epoch used is 20 and the hidden layer is 64. Following the classification process, this study concludes that the two methods are appropriate for classifying public conversation sentences against Covid-19. According to this study, the LSTM method is superior, with an accuracy of 83.3%, a precision of 85.6%, a recall of 90.6%, and an f1-score of 88.5%. While the CNN method achieved an accuracy of 81%, precision of 71.7%, recall of 72%, and f1-score of 72%.

Determining community happiness index with transformers and attention-based deep learning

“…The LSTM model is a development of the recurrent neural network (RNN) model to overcome vanishing gradient or exploding gradient problems [16]. With the existence of 3 gates, which include the input gate, the forget gate and the output gate, it can function to control the flow of information in and out of the memory cell [17]. After using LSTM, it is continued with the addition of an attention mechanism layer to improve the quality of predictions or outputs produced by focusing on the most influential parts of the final result [18].…”