scholarly journals Sales Prediction by Integrating Heat and Sentiments of Product Dimensions

2018 ◽  
Author(s):  
Xiaozhong Lyu ◽  
Cuiqing Jiang ◽  
Yong Ding ◽  
Zhao Wang ◽  
Yao Liu
Keyword(s):  
Big Data ◽  
Empirical Study ◽  
Prediction Model ◽  
Word Of Mouth ◽  
Prediction Models ◽  
Movie Industry ◽  
Topic Analysis ◽  
Box Office ◽  
Online Word Of Mouth ◽  
Sales Prediction

The accuracy of sales prediction models based on the big data of online word-of-mouth (eWOM) is still not satisfied. We argue that eWOM contains heat and sentiments of different product dimensions, which can improve the accuracy of these models. In this paper, we propose a dynamic topic analysis (DTA) framework in order to extract heat and sentiments of product dimensions from the big data of eWOM. Finally, we propose an autoregressive-heat-sentiment (ARHS) model, which integrates heat and sentiments of dimensions into the baseline predictive model. The empirical study in movie industry confirms that heat and sentiments of dimensions can improve the accuracy of sales prediction model. ARHS model is better for movie box-office revenue prediction than other models.

scholarly journals Sales Prediction by Integrating Heat and Sentiments of Product Dimensions

2018 ◽  
Author(s):  
Xiaozhong Lyu ◽  
Cuiqing Jiang ◽  
Yong Ding ◽  
Zhao Wang ◽  
Yao Liu
Keyword(s):  
Big Data ◽  
Empirical Study ◽  
Prediction Model ◽  
Word Of Mouth ◽  
Prediction Models ◽  
Movie Industry ◽  
Topic Analysis ◽  
Box Office ◽  
Online Word Of Mouth ◽  
Sales Prediction

The accuracy of sales prediction models based on the big data of online word-of-mouth (eWOM) is still not satisfied. We argue that eWOM contains heat and sentiments of different product dimensions, which can improve the accuracy of these models. In this paper, we propose a dynamic topic analysis (DTA) framework in order to extract heat and sentiments of product dimensions from the big data of eWOM. Finally, we propose an autoregressive-heat-sentiment (ARHS) model, which integrates heat and sentiments of dimensions into the baseline predictive model. The empirical study in movie industry confirms that heat and sentiments of dimensions can improve the accuracy of sales prediction model. ARHS model is better for movie box-office revenue prediction than other models.

scholarly journals Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions

2019 ◽  
Vol 11 (3) ◽  
pp. 913 ◽  
Author(s):  
Xiaozhong Lyu ◽  
Cuiqing Jiang ◽  
Yong Ding ◽  
Zhao Wang ◽  
Yao Liu
Keyword(s):  
Big Data ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Movie Industry ◽  
Box Office ◽  
Online Word Of Mouth ◽  
Sales Prediction ◽  
Accuracy Of Prediction ◽  
Better Than

Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used.

scholarly journals Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Prediction Model ◽  
Prediction Models ◽  
Meteorological Data ◽  
Heavy Rain ◽  
Machine Learning Techniques ◽  
Damage Prediction ◽  
Explanatory Variables ◽  
The Republic

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.

Box office performance: Influence of online word-of-mouth on consumers’ motivations to watch movies

2019 ◽  
Vol 47 (10) ◽  
pp. 1-17
Author(s):  
Kuo-Ting Yu ◽  
Hsi-Peng Lu ◽  
Chih-Yu Chin ◽  
Yu-Shiuan Jhou
Keyword(s):  
Intrinsic Motivation ◽  
Structural Equation ◽  
Word Of Mouth ◽  
Positive Impact ◽  
Positive Influence ◽  
Production Costs ◽  
Equation Modeling ◽  
Box Office ◽  
Online Word Of Mouth ◽  
Marketing Budgets

Content created by the movie industry is high-risk, as production costs and marketing budgets are massive but box office results can be volatile. We examined the effect of online word-of-mouth on consumers’ motivation and intention to watch movies. The proposed model was tested in a survey with 337 consumers using a structural equation modeling approach. The results showed that movie reviews by professional movie media writers had a substantial positive impact on consumers’ intrinsic motivation for presenting themselves, via transmitting their values and expressing personal favor by watching movies. Popular media also had a positive influence on the intrinsic motivation of perceived enjoyment, and social media had the broadest influence on consumers’ intrinsic motivation. Thus, movie makers and marketers should focus on the critical platform of online word-ofmouth to enhance consumers’ motivation to watch movies.

Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry

2020 ◽  
Vol 66 (5) ◽  
pp. 2140-2162 ◽  
Author(s):  
Shijie Lu ◽  
Xin (Shane) Wang ◽  
Neil Bendle
Keyword(s):  
Empirical Analysis ◽  
Word Of Mouth ◽  
Movie Industry ◽  
Online Word Of Mouth

scholarly journals Movie Production Efficiency Moderating between Online Word-of-Mouth and Subsequent Box Office Revenue

2020 ◽  
Vol 12 (16) ◽  
pp. 6602
Author(s):  
Sangjae Lee ◽  
Joon Yeon Choeh
Keyword(s):  
Production Efficiency ◽  
Word Of Mouth ◽  
Linear Regression Analysis ◽  
Ensemble Methods ◽  
Prediction Performance ◽  
Negative Effects ◽  
K Nearest Neighbors ◽  
Box Office ◽  
Online Word Of Mouth ◽  
The Relationship

The studies are almost nonexistent regarding production efficiency of movies which is determined based on the relationship between movie resources powers (powers of actors, directors, distributors, and production companies) and box office. Our study attempts to examine how efficiency moderates the relationship between eWOM (online word-of-mouth) and revenue, and to show the difference in prediction performance between efficient and inefficient movies. Using data envelopment analysis to suggest efficiency of movies, movie efficiency negatively moderates the effects of review depth and volume on subsequent box office revenue compensating negative effects of smaller box office in previous period while efficiency exert a positive moderating effect on the influences of review rating and the number of positive reviews on revenue. This shows that review depth and volume are affected by the slack of movie resources powers for inefficient movies, and high rating and positive response for efficient movies to affect revenue. The results of decision trees, k-nearest-neighbors, and linear regression analysis based on ensemble methods using eWOM or movie variables indicate that the movies with the inefficient movie resources powers are providing greater prediction performance than movies with efficient movie resources powers. This show that diverse variation in the efficiency of movie resources powers contributes to prediction performance.

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