Box office forecasting using machine learning algorithms based on SNS data

Kim, Tae-Gu; Hong, Jun; Kang, Pilsung

doi:10.1016/j.ijforecast.2014.05.006

Cited by 93 publications

(50 citation statements)

References 43 publications

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“…Jang et al 2013Deep sentiment analysis The proposed method can determine customers' value structure and causality for identifying a niche market. Kim et al (2015) Forecasting earning Social network information and a machine learning algorithm can improve forecasting accuracy. Targeted advertising The effectiveness of advertisement can be enhanced by leveraging social context and social influence.…”

Section: Tables Table 1 List Of Studies On Web and Mobile Analytics mentioning

confidence: 99%

Technology in the 21st century: New challenges and opportunities

Sheng

Amankwah‐Amoah

Wang

2019

Technological Forecasting and Social Change

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Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research.

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Section: Tables Table 1 List Of Studies On Web and Mobile Analytics mentioning

confidence: 99%

Technology in the 21st century: New challenges and opportunities

Sheng

Amankwah‐Amoah

Wang

2019

Technological Forecasting and Social Change

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“…Recent research has also shown that customer reviews can have a positive influence on sales (Chevalier and Mayzlin 2006;Du, Xu, and Huang 2014;Ghose and Ipeirotis 2011;Kim, Hong, and Kang 2015;Kim, Park, and Park 2013;Liu et al 2014;Rui, Liu, and Whinston 2013). One area in need of further examination is what makes an online review helpful to consumers.…”

Section: Introductionmentioning

confidence: 99%

The determinants of helpfulness of online reviews

Lee

Choeh

2016

Behaviour & Information Technology

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More and more people are gravitating to reading online product reviews prior to making purchasing decisions. Because a number of reviews that vary in usefulness are posted every day, much attention is being paid to measuring their helpfulness. The goal of this paper is to investigate the various determinants of the helpfulness of reviews, and it also intends to examine the moderating effect of product type, that is, the experience or search goods in relation to the helpfulness of online reviews. The study results show that reviewer reputation, the disclosure of reviewer identity, and review depth positively affect the helpfulness of an online review. The moderating effects of product type exist for these determinants on helpfulness. That is, the number of reviews for a product and the disclosure of reviewer identity have a greater influence on the helpfulness for experience goods, while reviewer reputation, review extremity, and review depth are more important for helpfulness in relation to search goods. The interaction effects exist for average review rating and average review depth for a product with review helpfulness on product sales. The results of the study will identify helpful online reviews and assist in designing review sites effectively. ARTICLE HISTORY

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“…Predictions of box-office revenue can be improved by also using an online prediction game and an online prediction market (McKenzie, 2013). The use of more data often provides greater accuracy in forecasts, particularly when using behavioural data from social network services (SNS) (Kim, Hong, & Kang, 2015). The use of such 'secondary search data' can enable firms to predict sales better (Chandukala, Dotson, Liu, & Conrady, 2014).…”

Section: Discussion Can Be Used To Predict Retail Salesmentioning

confidence: 99%

Think exogenous to excel: alternative supply chain data to improve transparency and decisions

Wood

Reiners

Srivastava

2016

International Journal of Logistics Research and Applications

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Efficient decisions along the supply chain have traditionally demanded sophisticated information sharing processes. Even with decades of research on theoretical and practical developments on integrating systems and stakeholders, in practice, we still seem to struggle to achieve full transparency and mitigate inefficiency challenges. We explore the emerging sentiment analysis technique to augment sales and operations planning (S&OP) with currently unavailable exogenous information. Even though sentiment analysis has gained traction, a comprehensive application in supply chains has not yet been attempted. Relevant topics are reviewed to allow an examination of the key relationships in a process framework, grounded in dual-process and bullwhip effect theory. Our proposed conceptual framework extends our conception of sentiment analysis integration to improve supply chain decisions and performance. The framework addresses managers interested in developing additional analytical capabilities and researchers to initiate further empirical research on the potential held by sentiment analysis in supply chain research.

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Box office forecasting using machine learning algorithms based on SNS data

Cited by 93 publications

References 43 publications

Technology in the 21st century: New challenges and opportunities

Technology in the 21st century: New challenges and opportunities

The determinants of helpfulness of online reviews

Think exogenous to excel: alternative supply chain data to improve transparency and decisions

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