The paper was trying to extract entities from related tweets collected from twitter. This project first collected real-time tweets from twitter searching API with related topic-based hashtags during the death of American black man George Floyd. We then used two approaches to identify the polarities or emotions of each tweets and generated over-time sentiment flow chart in detecting entities. We found that some extreme sentiment score was correlated with some key entities over time. And our adapted NRC-lexicon based approach obtained better results. This paper revealed that public’s sentiment displayed on tweets was generally consistent with the correlated events previously. It might help researchers in predicting or preventing public events in the future.
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