Predicting Movie Success Based On Imdb Data

Abstract

Film studios in the America, every year produce several hundred movies that make the United States the third most abundant producer of films in the world. The budget of these movies is of the order of hundreds of millions of dollars, making their box office success absolutely essential for the survival of the industry. Knowing which movies are likely to succeed and which are likely to fail before the release could benefit the production houses greatly as it will enable them to focus their advertising campaigns which itself cost millions of dollars, accordingly. And it could also help them to know when it is most appropriate to release a movie by looking at the overall market. So, the prediction of movie success is of great importance to the industry. Machine learning algorithms are widely used to make predictions such as growth in the stock market, demand for products, nature of tumors, etc. This paper presents a detailed study of logistic regression, SVM regression, and linear regression on IMDb data to predict movie box office.

Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias

“…In film credibility regarding the film industry, film reviews such as IMDb movie review sites cannot be separated because of their high credibility [1]. With the IMDb site frequently becoming a reference to rate films circulating in media with varied genres and more prominent communities to review the said film, IMDb is also becoming one factor in how the film itself has value [1]. IMDb also has a function for finding film references to watch and pulling audiences for recommended films from IMDb communities.…”

“…Research [1] uses IMDb dataset to determine the film's success with basic machine learning models such as Linear Regression, Logistic Regression, and Support Vector Machine (SVM) with an accuracy of 51% for Linear Regression, 42.2% for Logistic Regression, and 39% for SVM respectively. The problem in research [1] is the lack of depth on methods being restricted to only basic approaches and disadvantages showing low accuracies through sentiment classification. The topic is then further explored in research [3] as Hybrid Feature Extraction (Machine Learning and Lexicon-based methods) is studied, resulting in a decent performance increase from previous research.…”