Building a TSA model that can automatically determine the sentiment of a tweet has received significant attention over the past several years.
since most state-of-the-art TSA models use machine learning to tune their parameters, their performance -and relevance to a real-world implementation setting -is highly dependent on the dataset on which they are trained.