An Exploratory Data Analysis of Movie Review Dataset
V. Vanitha1, V. P. Sumathi2, V. Soundariya3
1V. Vanitha, Professor, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
2V. P. Sumathi, Assistant Professor, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
3V. Soundariya, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 14 December 2018 | Revised Manuscript received on 25 December 2018 | Manuscript Published on 09 January 2019 | PP: 380-384 | Volume-7 Issue-4S November 2018 | Retrieval Number: E2008017519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The film industry plays a major role in the planetary or world-wide economy. It is the symbolic contributor to the global economy. Every year more than hundreds to thousands of movies are released to the public audience with the hope that the movies getting released will be the next block buster. According to the movie industry statistics, six to seven movies out of ten movies gets unprofitable, only one third of the movie gets success. The producers, studios, investors, sponsors in the movie industry are alike interested in predicting the box office success of the movie. This paper work is on analysing the film genre, the release date around holidays, the release month of movies, the languages and country with more movies from the movie review dataset. There are attributes (country, languages, genre, movie release date, budget and revenue) taken from the dataset and the derived attributes (release month of the movie derived from release date of movie and profit from budget and revenue) is analysed to determine the movie performance. The analysed data is plotted in graphs for statistical observation of the movie success.
Keywords: Predicting Box Office Success, Block Buster, Film Genre, Genre Count, Release Month, Movie Profit, and Movie Review Dataset.
Scope of the Article: Data Analytics