Forecasting sales in the supply chain: Consumer analytics in the big data era

Box-office forecasting based on sentiments of movie reviews and Independent subspace method

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.

The determinants of helpfulness of online reviews

Purpose




While a number of studies examined the eWOM (online word-of-mouth) factors affecting box office, the studies on the impact of review helpfulness on box office are lacking. The purpose of this paper is to fill the void in previous studies and further extend prior work regarding eWOM and box office. In order to explain the interaction effect of helpfulness with other variables on product sales, this study posits that review characteristics such as number of reviews, review rating, review length interact with review helpfulness to have an influence on box office. Further, as the studies that have examined whether eWOM factors are significant in box office performances for the international markets other than US are lacking, this study is targeting Korean markets to validate the effect of eWOM on box office.




Design/methodology/approach




This study used publicly available data from www.naver.com to build a sample of online review data concerning box office. The final sample of the study included 2090 movies.




Findings




The results indicated that in cases when the review is helpful, the number of reviews and review length are more greatly influencing box office. Review rating, review extremity, and helpfulness for reviewer are important determinants for review helpfulness.




Practical implications




Managers can concentrate on the review rating and review extremity of online customer reviews in the design of online sites for movies. The design of user review systems can follow the direction that promotes more helpfulness for online user reviews based on an enhanced understanding of what drives helpfulness voting.




Originality/value




Given that previous studies on the effect of review helpfulness on box office are lacking, it contributes to eWOM literature by investigating the impact of review helpfulness on box office revenue.

The interactive impact of online word-of-mouth and review helpfulness on box office revenue

Predicting society's reaction to a new product in the sense of popularity and adaption rate has become an emerging field of data analysis The motion picture industry is a multi-billion-dollar business, and there is a massive amount of data related to movies is available over the internet This study proposes a decision support system for movie investment sector using machine learning techniques This research helps investors associated with this business for avoiding investment risks The system predicts an approximate success rate of a movie based on its profitability by analyzing historical data from different sources like IMDb, Rotten Tomatoes, Box Office Mojo and Metacritic Using Support Vector Machine (SVM), Neural Network and Natural Language Processing the system predicts a movie box office profit based on some pre-released features and post-released features This paper shows Neural Network gives an accuracy of 841% for pre-released features and 8927% for all features while SVM has 8344% and 8887% accuracy for pre-released features and all features respectively when one away prediction is considered Moreover, we figure out that budget, IMDb votes and no of screens are the most important features which play a vital role while predicting a movie's box-office success

/pdf/a-machine-learning-approach-to-predict-movie-box-office-2liuvo844t.pdf

A machine learning approach to predict movie box-office success

Predicting the box-office revenue of a movie before its theatrical release is an important but challenging problem that requires a high level of Artificial Intelligence. Nowadays, social media has shown its predictive power in various domains, which motivates us to exploit social media content to predict box-office revenues. In this study, we employ both linear and non-linear regression models, which are based on the crowd wisdom of social media, especially the posts of users, to predict movie box-office revenues. More specifically, the attention and popularity of the movie, purchase intention of users, and comments of users are automatically mined from social media data. In our model, the use of Linear Regression and Support Vector Regression in predicting the box-office revenue of a movie before its theatrical release is explored. To evaluate the effectiveness of the proposed approach, a cross-validation experiment is conducted. The experimental results show that large-scale social media content is correlated with movie box-office revenues and that the purchase intention of users can lead to more accurate movie box-office revenue predictions. Both the linear and non-linear prediction models have the advantage of predicting movie grosses in our experiments.

Predicting movie Box-office revenues by exploiting large-scale social media content

User generated content on social media has attracted much attention from service/product providers, as it contains plenty of potential commercial opportunities. However, previous work mainly focuses on user consumption intention (CI) identification, and little effort has been spent to mine intention-related products. In this paper, focusing on the Baby & Child Care domain, we propose a novel approach to mine intention-related products on online question and answer (Q&A) community. Making use of the question-answering pairs as data source, we first automatically extract candidate products based on dependency parser. And then by means of the collocation extraction model, we identify the real intention-related products from the candidate set. The experimental results on our carefully constructed evaluation dataset show that our approach achieves better performance than two natural baseline methods.

Mining Intention-Related Products on Online Q&A Community

The recognition of long-distance runners is mainly used to retrieve specific long-distance runner targets across video equipment in long-distance running events, which helps to improve the efficiency of the management of long-distance running events. At present, with the rapid development of artificial intelligence, lots of scholars utilize deep learning-based Person Re-Identification(Re-ID) technology to achieve long-distance runner recognition tasks. However, in practical applications, problems such as occlusion, noise, brightness changes, and color shifts usually affect the collected images of long-distance runners, thereby reducing the recognition accuracy of the existing Re-ID technology. For this reason, this paper proposes a recognition network for long-distance runners named Ldrr-net based on deep learning.Ldrr-net introduces the IGBN structure into the backbone network called Resnet50, which can reduce the adverse effects caused by the captured images, and has stronger robustness. In addition, we modify the loss, and propose Ldrr-loss to train network parameters, so that the network can better achieve intra-class aggregation and inter-class separation in the case of occlusion and similar features, and further improve the accuracy of long-distance runners' recognition. Experiments show that Ldrr-net has certain advantages in the recognition task of long-distance runners.

Yi-Heng Chen

Papers

Predicting movie Box-office revenues by exploiting large-scale social media content

Mining Intention-Related Products on Online Q&A Community

Research and Implementation of Recognition Algorithm of Long-distance Runners Based on Deep Learning