Movie Reviews and Revenues: An Experiment in Text Regression

Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, pages 293–296, Los Angeles, California, June 2010. c⃝2010 Association for Computational Linguistics Movie Reviews and Revenues: An Experiment in Text Regression∗ Mahesh Joshi Dipanjan Das Kevin Gimpel Noah A. Smith Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213, USA {maheshj,dipanjan,kgimpel,nasmith}@cs.cmu.edu Abstract We consider the problem of predicting a movie’s opening weekend revenue. Previous work on this problem has used metadata about a movie—e.g., its genre, MPAA rating, and cast—with very limited work making use of text about the movie. In this paper, we use the text of film critics’ reviews from several sources to predict opening weekend revenue. We describe a new dataset pairing movie re- views with metadata and revenue data, and show that review text can substitute for meta- data, and even improve over it, for prediction. 1 Introduction Predicting gross revenue for movies is a problem that has been studied in economics, marketing, statistics, and forecasting. Apart from the economic value of such predictions, we view the forecasting problem as an application of NLP. In this paper, we use the text of critics’ reviews to predict opening weekend revenue. We also consider metadata for each movie that has been shown to be successful for similar prediction tasks in previous work. There is a large body of prior work aimed at pre- dicting gross revenue of movies (Simonoff and Spar- row, 2000; Sharda and Delen, 2006; inter alia). Cer- tain information is used in nearly all prior work on these tasks, such as the movie’s genre, MPAA rating, running time, release date, the number of screens on which the movie debuted, and the presence of partic- ular actors or actresses in the cast. Most prior text- based work has used automatic text analysis tools, deriving a small number of aggregate statistics. For example, Mishne and Glance (2006) applied sen- timent analysis techniques to pre-release and post- release blog posts about movies and showed higher ∗We appreciate reviewer feedback and technical advice from Brendan O’Connor. This work was supported by NSF IIS- 0803482, NSF IIS-0844507, and DARPA NBCH-1080004. correlation between actual revenue and sentiment- based metrics, as compared to mention counts of the movie. (They did not frame the task as a revenue prediction problem.) Zhang and Skiena (2009) used a news aggregation system to identify entities and obtain domain-specific sentiment for each entity in several domains. They used the aggregate sentiment scores and mention counts of each movie in news articles as predictors. While there has been substantial prior work on using critics’ reviews, to our knowledge all of this work has used polarity of the review or the number of stars given to it by a critic, rather than the review text directly (Terry et al., 2005). Our task is related to sentiment analysis (Pang et al., 2002) on movie reviews. The key difference is that our goal is to predict a future real-valued quan- tity, restricting us from using any …

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