Social media sentiment analysis based on COVID-19

• Main Authors: László Nemes, Attila Kiss
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-01-01
• Series: Journal of Information and Telecommunication
• Subjects: natural language processing; recurrent neural network; sentiment analysis; social media; visualization
• Online Access: http://dx.doi.org/10.1080/24751839.2020.1790793
• ISSN: 2475-1839; 2475-1847

In today's world, the social media is everywhere, and everybody come in contact with it every day. With social media datas, we are able to do a lot of analysis and statistics nowdays. Within this scope of article, we conclude and analyse the sentiments and manifestations (comments, hastags, posts, tweets) of the users of the Twitter social media platform, based on the main trends (by keyword, which is mostly the ‘covid’ and coronavirus theme in this article) with Natural Language Processing and with Sentiment Classification using Recurrent Neural Network. Where we analyse, compile, visualize statistics, and summarize for further processing. The trained model works much more accurately, with a smaller margin of error, in determining emotional polarity in today's ‘modern’ often with ambiguous tweets. Especially with RNN. We use this fresh scraped data collections (by the keyword's theme) with our RNN model what we have created and trained to determine what emotional manifestations occurred on a given topic in a given time interval.