Paper Info
Title
Issue-Focused Documentaries versus Other Films: Rating and Type Prediction based on User-Authored Reviews.
Abstract
User-authored reviews offer a window into micro-level engagement with issue-focused documentary films, which is a critical yet insufficiently understood topic in media impact assessment. Based on our data, features, and supervised learning method, we find that ratings of non-documentary (feature film) reviews can be predicted with higher accuracy (73.67%, F1 score) than ratings of documentary reviews (68.05%). We also constructed a classifier that separates reviews of documentaries from reviews of feature films with an accuracy of 71.32%. However, as our goal with this paper is not to improve the accuracy of predicting the rating and type or genre of film reviews, but to advance our understanding of the perception of documentaries in comparison to feature films, we also identified commonalities and differences between these two types of films as well as between low versus high ratings. We find that in contrast to reviews of feature films, comments on documentaries are shorter but composed of longer sentences, are less emotional, contain less positive and more negative terms, are lexically more concise, and are more focused on verbs than on nouns and adjectives. Compared to low-rated reviews, comments with a high rating are shorter, are more emotional and contain more positive than negative sentiment, and have less question marks and more exclamation points. Overall, this work contributes to advancing our understanding of the impact of different types of information products on individual information consumers.
Year
DOI
Venue
2016
10.1145/2914586.2914638
HT
Keywords
Field
DocType
Rating prediction, Type prediction, Documentary films, Social impact
F1 score,Impact assessment,World Wide Web,Computer science,Noun,Supervised learning,Classifier (linguistics),Multimedia,Perception,Social impact
Conference
Citations 
PageRank 
References 
0
0.34
22
Authors
2
Name
Order
Citations
PageRank
Ming Jiang1100.99
Jana Diesner221624.38