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.
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This study was supported in part by the Ministry of Science and Technology of Taiwan under the Grants NSC 102-2410-H-031-058-MY3 and MOST 105-2410-H-031-035-MY3.
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Cheng, LC., Huang, CL. Exploring contextual factors from consumer reviews affecting movie sales: an opinion mining approach. Electron Commer Res 20, 807–832 (2020). https://doi.org/10.1007/s10660-019-09332-z
- Opinion mining
- Sentiment analysis
- Temporal abstraction
- Consumer reviews