Electronic Word of Mouth (eWOM) in the movie industry

Ho Lee; Hong Joo Lee; Ruth Angelie Cruz; John Laurence Enriquez

doi:10.1145/2971603.2971610

Electronic Word of Mouth (eWOM) in the movie industry

Ho Lee, Hong Joo Lee, Ruth Angelie Cruz, John Laurence Enriquez

Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This developmental paper aims to make use of Big Data, to establish how Electronic Word-of-Mouth or eWOM affects consumer behavior in the movie industry. For the purpose of this study, 134,998 reviews were collected for 1,238 movies. These reviews were taken from the IMDb website for movies shown from 2011 to 2013. To collect the extensive number of eWOM messages used for this paper's dataset, a program utilizing the Python programming language was used to crawl and collect movie reviews and other relevant information. Sentiment Analysis making use of the Semantic Orientation Calculator (SO-CAL) would then be used to calculate the valence of the reviews and compare them to the star ratings given by the reviewers.

Original language	English
Title of host publication	Proceedings of the 18th Annual International Conference on Electronic Commerce
Subtitle of host publication	e-Commerce in Smart connected World, ICEC 2016
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450342223
DOIs	https://doi.org/10.1145/2971603.2971610
State	Published - 17 Aug 2016
Event	18th International Conference on Electronic Commerce, ICEC 2016 - Suwon, Korea, Republic of Duration: 17 Aug 2016 → 19 Aug 2016

Publication series

Name	ACM International Conference Proceeding Series
Volume	17-19-August-2016

Conference

Conference	18th International Conference on Electronic Commerce, ICEC 2016
Country/Territory	Korea, Republic of
City	Suwon
Period	17/08/16 → 19/08/16

Bibliographical note

Publisher Copyright:
© 2016 ACM.

Keywords

Big data
Data mining
Online reviews
Sentiment analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/2971603.2971610

Cite this

Lee, H., Lee, H. J., Cruz, R. A., & Enriquez, J. L. (2016). Electronic Word of Mouth (eWOM) in the movie industry. In Proceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart connected World, ICEC 2016 Article 2971610 (ACM International Conference Proceeding Series; Vol. 17-19-August-2016). Association for Computing Machinery. https://doi.org/10.1145/2971603.2971610

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title = "Electronic Word of Mouth (eWOM) in the movie industry",

abstract = "This developmental paper aims to make use of Big Data, to establish how Electronic Word-of-Mouth or eWOM affects consumer behavior in the movie industry. For the purpose of this study, 134,998 reviews were collected for 1,238 movies. These reviews were taken from the IMDb website for movies shown from 2011 to 2013. To collect the extensive number of eWOM messages used for this paper's dataset, a program utilizing the Python programming language was used to crawl and collect movie reviews and other relevant information. Sentiment Analysis making use of the Semantic Orientation Calculator (SO-CAL) would then be used to calculate the valence of the reviews and compare them to the star ratings given by the reviewers.",

keywords = "Big data, Data mining, Online reviews, Sentiment analysis",

author = "Ho Lee and Lee, \{Hong Joo\} and Cruz, \{Ruth Angelie\} and Enriquez, \{John Laurence\}",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 18th International Conference on Electronic Commerce, ICEC 2016 ; Conference date: 17-08-2016 Through 19-08-2016",

year = "2016",

month = aug,

day = "17",

doi = "10.1145/2971603.2971610",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

booktitle = "Proceedings of the 18th Annual International Conference on Electronic Commerce",

}

Lee, H, Lee, HJ, Cruz, RA & Enriquez, JL 2016, Electronic Word of Mouth (eWOM) in the movie industry. in Proceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart connected World, ICEC 2016., 2971610, ACM International Conference Proceeding Series, vol. 17-19-August-2016, Association for Computing Machinery, 18th International Conference on Electronic Commerce, ICEC 2016, Suwon, Korea, Republic of, 17/08/16. https://doi.org/10.1145/2971603.2971610

Electronic Word of Mouth (eWOM) in the movie industry. / Lee, Ho; Lee, Hong Joo; Cruz, Ruth Angelie et al.
Proceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart connected World, ICEC 2016. Association for Computing Machinery, 2016. 2971610 (ACM International Conference Proceeding Series; Vol. 17-19-August-2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - This developmental paper aims to make use of Big Data, to establish how Electronic Word-of-Mouth or eWOM affects consumer behavior in the movie industry. For the purpose of this study, 134,998 reviews were collected for 1,238 movies. These reviews were taken from the IMDb website for movies shown from 2011 to 2013. To collect the extensive number of eWOM messages used for this paper's dataset, a program utilizing the Python programming language was used to crawl and collect movie reviews and other relevant information. Sentiment Analysis making use of the Semantic Orientation Calculator (SO-CAL) would then be used to calculate the valence of the reviews and compare them to the star ratings given by the reviewers.

AB - This developmental paper aims to make use of Big Data, to establish how Electronic Word-of-Mouth or eWOM affects consumer behavior in the movie industry. For the purpose of this study, 134,998 reviews were collected for 1,238 movies. These reviews were taken from the IMDb website for movies shown from 2011 to 2013. To collect the extensive number of eWOM messages used for this paper's dataset, a program utilizing the Python programming language was used to crawl and collect movie reviews and other relevant information. Sentiment Analysis making use of the Semantic Orientation Calculator (SO-CAL) would then be used to calculate the valence of the reviews and compare them to the star ratings given by the reviewers.

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KW - Data mining

KW - Online reviews

KW - Sentiment analysis

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M3 - Conference contribution

AN - SCOPUS:84988712449

T3 - ACM International Conference Proceeding Series

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ER -

Electronic Word of Mouth (eWOM) in the movie industry

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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Cite this