Sentiment Analysis of COVID 19 Tweets using Optimize LSTM Model

The popularity of social media has increased curiosity in psychology, mental health, and human circumstances. Twitter and other social media platforms have been utilised for data collection, personality type prediction, and sentiment analysis during emergencies. Deep learning techniques examine both positive and negative feelings. The retrieval of Covid 19 tweets, both positive and negative, is not perfect, and low accuracy may lead to the detection of unidentified tweets from social sites. The objective is to improve the retrieval and comparison of pertinent Covid 19 positive and negative tweets in terms of precision, recall, and train/validation accuracy. A LSTM is defined by a sequence of layers, with the first layer defining inputs. The ADAM optimization algorithm updates weights, and the model is evaluated. The proposed model for sentiment content categorization from tweets uses LSTM-ADAM approaches, with a 78% accuracy rate compared to the 76% accuracy of SVM methodology. It supports manual participation and accepts text information in any format.

Covid-19, LSTM, ADAM, Accuracy

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