A sentiment-based digital library of movie review documents using Fedora

This study develops a digital library of movie review documents that supports sentiment-based browsing and searching. Firstly, we develop an automatic method for in-depth sentiment analysis and classification of movie review documents to provide sentiment orientations toward multiple perspectives of movies, such as overall opinion about the movie, director, and cast. By utilizing information extraction techniques such as entity extraction, co-referencing, and pronoun resolution, the review texts are segmented into multiple sections where each section contains multiple sentences and discusses a particular aspect of the reviewed movie. For each aspect section, a machine-learning algorithm, Support Vector Machine (SVM), is applied to determine sentiment orientation toward the target aspect. Secondly a prototype digital library is developed with the automatically analysed data to show the usefulness of sentiment-based browsing and searching. Using the system, the user can browse and search movies by sentiment polarity (positive, neutral, or negative) of multiple aspects in the movie. Finally, a usability evaluation is conducted to observe the effectiveness of the sentiment-based digital library.