IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v20y2020i4d10.1007_s10660-019-09332-z.html
   My bibliography  Save this article

Exploring contextual factors from consumer reviews affecting movie sales: an opinion mining approach

Author

Listed:
  • Li-Chen Cheng

    (Soochow University)

  • Chi-Lun Huang

    (Soochow University)

Abstract

In the age of Web 2.0, the rapid growth of user-generated content (e.g., consumer reviews) on the Internet offers ample avenues to search for information useful to both people and companies. Prior works in this field relating to movies have focused on the average rating and the number of comments. In this study, we used the content of consumer reviews and propose a novel framework integrating opinion mining and machine learning techniques to explore contextual factors influencing box-office revenue. Moreover, we analyzed movie review data from the website Internet Movie Database to examine the relationship among time periods, users’ opinion, and changes in box-office patterns. Experimental evaluations demonstrated that changes in different aspects of opinions effected a change in box-office revenue. Thus, movie marketers should monitor changes in the various aspects of online reviews and accordingly devise e-marketing strategies.

Suggested Citation

  • Li-Chen Cheng & Chi-Lun Huang, 2020. "Exploring contextual factors from consumer reviews affecting movie sales: an opinion mining approach," Electronic Commerce Research, Springer, vol. 20(4), pages 807-832, December.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:4:d:10.1007_s10660-019-09332-z
    DOI: 10.1007/s10660-019-09332-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-019-09332-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-019-09332-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    2. Sang Ho Kim & Namkee Park & Seung Hyun Park, 2013. "Exploring the Effects of Online Word of Mouth and Expert Reviews on Theatrical Movies' Box Office Success," Journal of Media Economics, Taylor & Francis Journals, vol. 26(2), pages 98-114, June.
    3. Biplab Bhattacharjee & Amulyashree Sridhar & Anirban Dutta, 2017. "Identifying the causal relationship between social media content of a Bollywood movie and its box-office success - a text mining approach," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 24(3), pages 344-368.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Yuntsai & Lin, Wei, 2024. "Blockbuster or Flop? Effects of Social Media on the Chinese Film Market," 24th ITS Biennial Conference, Seoul 2024. New bottles for new wine: digital transformation demands new policies and strategies 302460, International Telecommunications Society (ITS).
    2. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    3. Joe Cox & Daniel Kaimann, 2013. "The Signaling Effect of Critics - Evidence from a Market for Experience Goods," Working Papers CIE 68, Paderborn University, CIE Center for International Economics.
    4. Liuan Wang & Lu (Lucy) Yan & Tongxin Zhou & Xitong Guo & Gregory R. Heim, 2020. "Understanding Physicians’ Online-Offline Behavior Dynamics: An Empirical Study," Information Systems Research, INFORMS, vol. 31(2), pages 537-555, June.
    5. M. Narciso, 2022. "The Unreliability of Online Review Mechanisms," Journal of Consumer Policy, Springer, vol. 45(3), pages 349-368, September.
    6. Yuanfang Lin & Amit Pazgal, 2016. "Hide Supremacy or Admit Inferiority—Market Entry Strategies in Response to Consumer Informational Needs," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 3(2), pages 94-103, June.
    7. Gaenssle Sophia & Budzinski Oliver & Astakhova Daria, 2018. "Conquering the Box Office: Factors Influencing Success of International Movies in Russia," Review of Network Economics, De Gruyter, vol. 17(4), pages 245-266, December.
    8. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    9. Reinhold Decker, 2014. "Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(11), pages 60-70, November.
    10. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    11. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    12. Minnema, Alec & Bijmolt, Tammo H.A. & Gensler, Sonja & Wiesel, Thorsten, 2016. "To Keep or Not to Keep: Effects of Online Customer Reviews on Product Returns," Journal of Retailing, Elsevier, vol. 92(3), pages 253-267.
    13. Giovanni Bernardo & Massimo Ruberti & Roberto Verona, 2022. "Image is everything! Professional football players' visibility and wages: evidence from the Italian Serie A," Applied Economics, Taylor & Francis Journals, vol. 54(5), pages 595-614, January.
    14. Vimi Jham, 2023. "Filli Café: Experience Tea and Talk," Asian Journal of Management Cases, , vol. 20(1), pages 47-58, March.
    15. Baabdullah, Abdullah M. & Alalwan, Ali Abdallah & Algharabat, Raed S. & Metri, Bhimaraya & Rana, Nripendra P., 2022. "Virtual agents and flow experience: An empirical examination of AI-powered chatbots," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    16. Caldieraro, Fabio & Cunha, Marcus, 2022. "Consumers’ response to weak unique selling propositions: Implications for optimal product recommendation strategy," International Journal of Research in Marketing, Elsevier, vol. 39(3), pages 724-744.
    17. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    18. Yabing Jiang & Hong Guo, 2012. "Design of Consumer Review Systems and Product Pricing," Working Papers 12-10, NET Institute.
    19. Pradeep Kumar Ponnamma Divakaran & Jie Xiong, 2022. "Eliciting brand association networks: A new method using online community data," Post-Print hal-03700393, HAL.
    20. Angela Aerry Choi & Daegon Cho & Dobin Yim & Jae Yun Moon & Wonseok Oh, 2019. "When Seeing Helps Believing: The Interactive Effects of Previews and Reviews on E-Book Purchases," Information Systems Research, INFORMS, vol. 30(4), pages 1164-1183, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:20:y:2020:i:4:d:10.1007_s10660-019-09332-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.