Sentiment Analysis of COVID-19 Tweets Using BiLSTM and CNN-BiLSTM

ABSTRACT

In a society where people express almost every thought they have on social media, analysing social media for sentiment has become very significant in order to understand what the masses are thinking. Especially microblogging website like twitter, where highly opinionated individuals come together to discuss ongoing socioeconomic and political events happening in their respective countries or happening around the world. For analysing such vast amounts of data generated every day, a model with high efficiency, i.e., less running time and high accuracy, is needed. Sentiment analysis has become extremely useful in this regard. A model trained on a dataset of tweets can help determine the general sentiment of people towards a particular topic. This paper proposes a bidirectional long short-term memory (BiLSTM) and a convolutional bidirectional long short-term memory (CNN-BiLSTM) to classify tweet sentiment;the tweets were divided into three categories—positive, neutral and negative. Specialized word embeddings such as Word2Vec or term frequency—inverse document frequency (tf-idf) were avoided. The aim of this paper is to analyse the performance of deep neural network (DNN) models where traditional classifiers like logistic regression and decision trees fail. The results show that the BiLSTM model can predict with an accuracy of 0.84, and the CNN-BiLSTM model can predict with an accuracy of 0.80. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.