Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models

Abstract

The emergence of social media platforms, which contributed in activating the patterns of connection between individuals, leads to the availability of a huge amount of content such as text, images, and videos. Twitter is one of the most popular platforms of social media that encourage researchers to investigate people’s feelings and opinions among through sentiment analysis studies that elicited the interest of researchers in natural language processing field. Many techniques related to machine learning and deep learning models could be used to improve the efficiency and performance of sentiment analysis, especially in complex classification problems. In this paper, different models of long short-term memory recurrent neural network are used for the sentiment classification task. The input text was represented as vectors using Arabic pre-trained word embedding (Aravec). Experiments were conducted using different dimensions of Aravec on 15779 tweets about COVID-19 collected and labeled as positive and negative. The experimental results show an accuracy value of 98%.

Sentiment analysis of imbalanced Arabic data using sampling techniques and classification algorithms

“…Despite the significant increase in the number of infections and deaths, some people still believe that COVID-19 is a rumor. Twitter is a rich source of data for scholars who want to study emotions in depth [1].…”