Predicting the Near-Weekend Ticket Sales Using Web-Based External Factors and Box-Office Data

Moon, Seonghoon; Bae, Soong June; Kim, Songkuk

doi:10.1109/wi-iat.2014.113

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…The growing interest in models that exploit search query data (Goel et al , 2010; Moon et al , 2014) and social data (Song et al , 2014; Cao et al , 2015; Loeb et al , 2014; Karagiorgou et al , 2014; Tripathi et al , 2015), and their closeness with the model proposed by us in the following of the work, requires a review of the literature, although not exhaustive, about the subject matter, as it is proposed in Sections 3 and 4.…”

Section: Toward the Near Future: Social Trend-based Decisionsmentioning

confidence: 99%

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

D'Avanzo¹,

Pilato²,

Lytras³

2017

PROG

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Purpose An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science). Design/methodology/approach The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials. Findings The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches. Originality/value The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

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Section: Toward the Near Future: Social Trend-based Decisionsmentioning

confidence: 99%

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

D'Avanzo¹,

Pilato²,

Lytras³

2017

PROG

View full text Add to dashboard Cite

show abstract

“…M Mestyan et al 16 proved that the popularity of a movie could be predicted much before its release based on data extracted from the entry to the movie in Wikipedia, the online encyclopedia. S Moon et al 17 applied machine learning techniques and linear modeling to develop a model for predicting the near-weekend ticket sales and the ideal number of screens using web-based external factors, such as online reviews, star ratings, and search volume. S Thigale et al 18 used sentiment analysis of Twitter data for the hype creating among the mob and showed that social media expressed a collective knowledge which can yield a powerful and accurate indicator of future revenues.…”

Section: Related Workmentioning

confidence: 99%

An inside look into the complexity of box-office revenue prediction in China

Xiao

Chen³

et al. 2017

International Journal of Distributed Sensor Networks

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In this article, we discuss various elements contributing to exerting influence on box office in China, which are divided into internal and external factors. Since these factors could merely be quantified by online data sources partially or inaccurately, we propose that relativity analysis is more reasonable than precise revenue prediction. Trailer is selected as the combination of movie content and online behavior prior to releasing. Indexes from seven mainstream video websites are retrieved by the designed big data system which is integrated with the Internet of things technology. Correlation coefficients of different time periods are calculated. We apply multiple linear regression with stepwise method in modeling and prove that watching counts of 1 week before releasing on Youku is the barometer of market performance, especially the first week revenues. We also manifest the power of influential users through constructing Sina Weibo acquisition and analysis system.

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Customer Review Analysis: A Systematic Review

Davis

Tabrizi

2021

2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD)

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Predicting the Near-Weekend Ticket Sales Using Web-Based External Factors and Box-Office Data

Cited by 7 publications

References 11 publications

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

An inside look into the complexity of box-office revenue prediction in China

Customer Review Analysis: A Systematic Review

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