The purpose of this study is to detect these reviews’ complex emotions, visualize and analyze them. Movie reviewers’ moviescores and reviews can be analyzed with respect to their emotion content, aggregated and projected onto a movie, resulting in an emotion map for a movie. It is then possible for a moviegoer to choose a movie, not only on the basis of movie scores and reviews, but also on the basis of aggregated emotional outcome of a movie as reflected by its emotion map displaying certain emotion map patterns desirable for the moviegoer.
The authors use the hourglass of emotion model to find the emotional scores of words of a review, then they use singular value decomposition to reduce the data dimension into singular scores. Once, they have the emotional scores of reviews, the authors cluster them by using k-means algorithm to find similar emotional levels of movies. Finally, the authors use heat maps to visualize four dimensions in a figure.
The authors are able to find the emotional levels of movie reviews, represent them in single scores and visualize them. The authors look the similarities and dissimilarities of movies based on their genre, ranking and emotional statuses. They also find the closest emotion levels of movies to a given movie.
The authors detect complex emotions from the text and simply visualize them.
