Aspect-based sentiment analysis of user-generated content from a microblogging platform

This research presents a practical application of advanced natural language processing techniques to understand people’s feelings during the global Covid-19 pandemic, using a set of big data of over 547 thousand tweets. Companies often use sentiment analysis to process comments, product usage, social media posts, and more, in order to better understand user needs and preferences. In this research paper, aspect-based sentiment analysis is applied as one of the most recent and advanced subtypes of sentiment analysis. Aspect-based sentiment analysis is a modern natural language processing technique that does not perform sentiment detection at the level of the entire input text but individually over all the aspects detected in it. Therefore, this technique enables the precision of analyzing the user data set and making concrete conclusions about people’s feelings. This research aims to develop a software infrastructure for further work in natural language processing using the aspect-based sentiment analysis technique. The proposed process flow and data handling methods, as defined in this research, are designed to be easily adaptable to other data sets with minimal modifications.