Indian Journal of Science and Technology

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Beyond Words of Popularization Mining:Reviews on Comic Books Movies


Affiliations
1 Department of Computing and Information Systems, School of Science and Technology, Sunway University, Subang Jaya, Malaysia
 

Objectives: This paper aims to analyse the terms used by the of movie critics on popular comic book movies. From the analysis we are able to know what are the factors that most movie critics will focus at, whether the top movie critics review has an impact on the decision of general movie goers and from this will determine why the movies was a box office success. Methods: The research is done via text mining from SAS Enterprise Miner software which utilize the text analytics tool to analyse movie reviews by movie critics’ professionals. The analytical processes involved are text parsing, text filtering, text clustering and the text topic method. Correlation of terms is identified to help in determining the significance of each terms nested within the reviews. Findings: The findings show that the actors, the movie's characters and the film storyline are the most common type of terms that most movie critics are focusing on, thus this factors is what drives the reputation of the film and making it a blockbuster hit around the world. Application/Improvements: The results obtained can help the entertainment industry on their decision making on what to focus on when it comes to producing comic books superheroes into movies based on the sentiment analysis in identifying the postive and negative terms and their relationship with one another.
User

  • Anon. Deadpool (2016) - Box Office Mojo. Boxofficemojo.com. 2016.

  • Anon. Batman v Superman: Dawn of Justice (2016) - Box Office Mojo. Boxofficemojo.com. 2016.

  • Anon. Captain America: Civil War (2016) - Box Office Mojo. Boxofficemojo.com. 2016.

  • Anon. All Time Worldwide Opening Records at the Box Office. Boxofficemojo.com. 2017

  • Bagga, S. Text Analytics: Unlocking the Value of Unstructured Data, International Institute For Analytics. 2016.

  • Dupont N. Hollywood adaptations of comic books in a post-9/11 context: The economic and cultural factors. 2016.

  • Gabilliet J, Beaty B, Nguyen N. Of Comics and Men: A cultural history of American Comic Books. 1st ed. MS: University Press of Mississippi. 2010; 1(1):85–8.

  • Jadhavar A, Sirolikar P, Chakraborty G. Analysis of IMDB reviews for movies and television series using SAS Enterprise Miner and SAS Sentiment Analysis Studio. 2016. p. 1–17.

  • Lee A, Lim T. Opinions from University Students’ feedback using Text Analytics. The Journal of Information Technology and Industry. 2016; 4(1):26–33.

  • Lee A, Lim T, Leow S, Lim J. Second International Conference on Digital Information and Communication Technology and its Applications (DICTAP); 2017. p. 183–8.

  • Lee A, Lim T. Mining “What they talk about” for a private healthcare service provider. Archives of Business Research. 2017; 5(5):1–22.

  • Nicholas, C. K. W., & Lee, A. S. H. (2017). Voice of customers: Text analysis of hotel customer reviews (cleanliness, overall environment & value for money). In Proceedings of the 2017 International Conference on Big Data Research, (2017), pp. 104-111. ACM. Crossref.

  • Paquette G. Visual knowledge modeling for semantic web technologies: Models and ontologies. Hershey, PA: Information Science Reference; 2010. Crossref 1, 2.

  • Raymond J. Mooney and Un Yong Nahm, “Text Mining with Information Extraction”, Multilingualism and Electronic Language Management: Proceedings of the 4th International MIDP Colloquium, pp. 141-160, 2003. PMid:12543550

  • Schreiber G, Akkermans H, Anjewierden A, Hoog R, Shadbolt N, Velde V. Knowledge engineering and management. Cambridge, Mass: MIT Press; 2000.

  • Shilpa D. “A Review of Text Mining Techniques Associated with Various Application Areas” International Journal of Science and Research (IJSR), 2015; 4. 2461-2466.

  • Sklar R. Movie-made America: A cultural history of American Movies. New York: Vintage Books; 1994. PMCid: PMC2568182. PMCid:PMC2568182

  • Talib R, Kashif M, Ayesha S, Fatima F. Text Mining: Techniques, applications and issues. International Journal of Advanced Computer Science and Applications. 2016; 7(11):1–5. Crossref.


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  • Beyond Words of Popularization Mining:Reviews on Comic Books Movies

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Authors

Angela S. H. Lee
Department of Computing and Information Systems, School of Science and Technology, Sunway University, Subang Jaya, Malaysia
Kaza NS. M. Kamil
Department of Computing and Information Systems, School of Science and Technology, Sunway University, Subang Jaya, Malaysia
Shankaraar Narendranaath
Department of Computing and Information Systems, School of Science and Technology, Sunway University, Subang Jaya, Malaysia

Abstract


Objectives: This paper aims to analyse the terms used by the of movie critics on popular comic book movies. From the analysis we are able to know what are the factors that most movie critics will focus at, whether the top movie critics review has an impact on the decision of general movie goers and from this will determine why the movies was a box office success. Methods: The research is done via text mining from SAS Enterprise Miner software which utilize the text analytics tool to analyse movie reviews by movie critics’ professionals. The analytical processes involved are text parsing, text filtering, text clustering and the text topic method. Correlation of terms is identified to help in determining the significance of each terms nested within the reviews. Findings: The findings show that the actors, the movie's characters and the film storyline are the most common type of terms that most movie critics are focusing on, thus this factors is what drives the reputation of the film and making it a blockbuster hit around the world. Application/Improvements: The results obtained can help the entertainment industry on their decision making on what to focus on when it comes to producing comic books superheroes into movies based on the sentiment analysis in identifying the postive and negative terms and their relationship with one another.

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DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i25%2F118570