Text Classification with Automatic Detection of COVID-19 Symptoms from Twitter Posts Using Natural Language Programming (NLP)

Numerous nations have enacted total lockdowns in an effort to contain the Covid-19 pandemic, which is spreading quickly throughout the globe and claiming millions of people every day. As people tended to vent their emotions through social media during this time of lockdown, these channels were crucial in helping to distribute information about the pandemic around the globe. We created an experimental methodology to examine Twitter users’ reactions while taking into consideration the terms that are frequently used to refer to the epidemic, either directly or indirectly. In order to carry out the text classification, the TF-IDF method is upgraded (TF-IDCRF) in this study. The dataset involved with 44,995 tweets from all over the world and the DL approach is utilized for improving classification accuracy by addressing the issue in inadequate classification of feature category. Finally, the suggested approach is compared to two DL methods with TF-IDF algorithms with Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) and the better prediction of tweet category is determined in which GRU performs high accuracy as 92.4% than LSTM technique.