Sentiment analysis of Shakespeare’s plays using a deep learning technique

This study examined the sentiment movement of Shakespeare’s plays (four tragedies and five comedies) using a deep learning technique. Sentiment analyses have been used in several fields to extract aspects of opinions using sentiment dictionaries such as ANEW, AFFINE, and VADER, which involve an evaluation of a word list for sentiment analysis. Nowadays, however, as deep learning algorithms develop, it became possible to conduct a sentiment analysis by using deep learning algorithms. This study directly compared the output of a simple deep learning model (trained with tweeters) with the output of a sentiment dictionary, VADER, targeting Shakespeare’s plays. The results showed that the simple deep learning model led to a similar performance with VADER for Shakespeare’s tragedies and outperformed the sentiment dictionary especially for Shakespeare’s comedies.