Using Text Mining in Film Industry through Text Analytics on Reviews of the Movie Kingsman Series
Ji-Heon Song1, Sung-Jun Kim2

1Ji-Heon Song, Big Data Industry Security, Nam Seoul University Graduate School, Cheonan, Korea, East Asia.

2Sung-Jun Kim, Big Data Industry Security, Nam Seoul University Graduate School, Cheonan, Korea, East Asia.  

Manuscript received on 10 June 2019 | Revised Manuscript received on 17 June 2019 | Manuscript Published on 22 June 2019 | PP: 676-680 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H11130688S219/19©BEIESP

Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (

Abstract: The movie reviews are important to success in film industry. So, the objectives for text analytics are reviews and scores of the movies ‘Kingsman: The Secret Agent’ and ‘Kingsman: The Golden Circle’ (It is referred to as the following Kingsman series 1 and series2). These data were collected by crawling on the movie reviews page of Korea’s largest portal site. Morphological analysis and Frequency analysis were used as an analysis method. Crawling and analysis were used in Python, an open source program. Many studies using movie reviews focus on using complex and difficult analysis techniques or algorithms. However, this study confirms the important and objective reaction factors to movie viewers through frequency analysis using morphological analysis which is the most basic of text analytics. It shows that the viewer’s response to the next series of films can be predicted. Therefore, even the basic analysis method can be useful for the film industry if it is appropriately used. A more effective method can be found by comparing the results of this study with other methods of analysis. And, it is the basis for supporting the effectiveness of this study.

Keywords: Bigdata, Movie Review, Natural Language Processing(NLP), Text Analytics.
Scope of the Article: Natural Language Processing