Unsupervised Improving of Sentiment Analysis Using Global Target Context

Current approaches to document-level sentiment analysis rely on local information, e.g., the words within the given document. We try to achieve better performance by incorporating global context of the sentiment target (e.g., a movie or a product). We assume that sentiment labels of reviews about the same target are often consistent in some way. We model this consistency by Dirichlet distribution over sentiment labels and use it together with Maximum entropy classifier to gain significant improvement. This unsupervised extension increases the classification F-measure by almost 3% absolute on both Czech and English movie review datasets and outperforms the current state of the art.