This analysis by researchers at Nanyang Technological University in Singapore uses GDELT as a news baseline to evaluate sentiment analysis of tweets about China's One Belt One Road initiative.
With the rapid adoption of the Internet, fast-moving social media platforms have been able to extract and encapsulate real-time public sentiments on different entities. Real-time sentiment analysis on current dynamic events such as elections, global affairs and sports are essential in the understanding the public’s reaction to the states and trajectories of these events. In this paper, we aim to extract the sentiments of the Belt and Road Initiative from Twitter. Using aspect-based sentiment analysis, we were able to obtain the tweet’s sentiment polarity on the related aspect category to better understand the topics that were discussed. We have developed an end-to-end sentiment analysis system that collects relevant data from Twitter, processes it and visualizes it on an intuitive display. We employed a hybrid approach of symbolic and sub-symbolic techniques using gated convolutional networks, aspect embeddings and the SenticNet framework to solve the subtasks of aspect category detection and aspect category polarity. A confidence score threshold was used to decide on the results provided by the models from the differing approaches.