Movie Revenue Prediction Based on Purchase Intention Mining Using YouTube Trailer Reviews
TL;DR: This paper builds a model for movie revenue prediction prior to the movie's release using YouTube trailer reviews and proves the superiority of this approach compared to three baseline approaches and achieved a relative absolute error of 29.65%.
Abstract: The increase in acceptability and popularity of social media has made extracting information from the data generated on social media an emerging field of research. An important branch of this field is predicting future events using social media data. This paper is focused on predicting box-office revenue of a movie by mining people's intention to purchase a movie ticket, termed purchase intention, from trailer reviews. Movie revenue prediction is important due to risks involved in movie production despite the high cost involved in the production. Previous studies in this domain focus on the use of twitter data and IMDB reviews for the prediction of movies that have already been released. In this paper, we build a model for movie revenue prediction prior to the movie's release using YouTube trailer reviews. Our model consists of novel methods of calculating purchase intention, positive-to-negative sentiment ratio, and like-to-dislike ratio for movie revenue prediction. Our experimental results prove the superiority of our approach compared to three baseline approaches and achieved a relative absolute error of 29.65%.
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21 Jul 2021
TL;DR: A hybrid convolutional neural network-long short-term memory (CNN-LSTM) model is proposed for sentiment analysis, which demonstrates that the proposed model outperforms with 91.3% accuracy in sentiment analysis.
Abstract: With the fastest growth of information and communication technology (ICT), the availability of web content on social media platforms is increasing day by day. Sentiment analysis from online reviews drawing researchers’ attention from various organizations such as academics, government, and private industries. Sentiment analysis has been a hot research topic in Machine Learning (ML) and Natural Language Processing (NLP). Currently, Deep Learning (DL) techniques are implemented in sentiment analysis to get excellent results. This study proposed a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for sentiment analysis. Our proposed model is being applied with dropout, max pooling, and batch normalization to get results. Experimental analysis carried out on Airlinequality and Twitter airline sentiment datasets. We employed the Keras word embedding approach, which converts texts into vectors of numeric values, where similar words have small vector distances between them. We calculated various parameters, such as accuracy, precision, recall, and F1-measure, to measure the model’s performance. These parameters for the proposed model are better than the classical ML models in sentiment analysis. Our results analysis demonstrates that the proposed model outperforms with 91.3% accuracy in sentiment analysis.
51 citations
TL;DR: In this article, the authors focus on the predictive power of online reviews on fundraising outcomes and find that sentiment strength is lower for successfully funded projects than for failed projects. But, as the fundraising progresses, sentiment in the comments gradually turns negative, and sentiment strength was found to be lower for successful funded projects.
18 citations
TL;DR: In this article , the authors focus on the predictive power of online reviews on fundraising outcomes and find that sentiment strength is lower for successfully funded projects than for failed projects. But, as the fundraising progresses, sentiment in the comments gradually turns negative, and sentiment strength was found to be lower for successful funded projects.
13 citations
TL;DR: A systematic literature review based on 109 high-quality research papers selected after rigorous screening reveals that there exist eight prominent categories of intention, and a taxonomy of the approaches and techniques used for intention mining is discussed.
Abstract: Intention mining is a promising research area of data mining that aims to determine end-users’ intentions from their past activities stored in the logs, which note users’ interaction with the system Search engines are a major source to infer users’ past searching activities to predict their intention, facilitating the vendors and manufacturers to present their products to the user in a promising manner This area has been consistently getting pertinence with an increasing trend for online purchasing Noticeable research work has been accomplished in this area for the last two decades There is no such systematic literature review available that provides a comprehensive review in intension mining domain to the best of our knowledge This article presents a systematic literature review based on 109 high-quality research papers selected after rigorous screening The analysis reveals that there exist eight prominent categories of intention Furthermore, a taxonomy of the approaches and techniques used for intention mining have been discussed in this article Similarly, six important types of data sets used for this purpose have also been discussed in this work Lastly, future challenges and research gaps have also been presented for the researchers working in this domain
12 citations
TL;DR: In this paper, an online questionnaire was applied through social networks, obtaining a sample of 334 Mexican people over 18 years old, and the data were analyzed using a partial least squares structural equation model (PLS-SEM).
Abstract: The present research aims to determine which factors of the theory of planned behavior most influence the intention to watch Mexican movies, and, at the same time, to measure the impact of eWOM and the level of audience involvement in the intention. For this purpose, an online questionnaire was applied through social networks, obtaining a sample of 334 Mexican people over 18 years old. The data were analyzed using a partial least squares structural equation model (PLS-SEM). The results confirmed that the variables that explained the intention to watch Mexican movies were attitude, perceived purchase control, and involvement, with the latter being the attitude variable the one that contributed the most to intention. The present research contributes to the literature on movie consumption in Mexico with an empirical perspective from the marketing field.
10 citations
References
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31 Aug 2010
TL;DR: It is shown that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors and improve the forecasting power of social media.
Abstract: In recent years, social media has become ubiquitous and important for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped. In this paper, we demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors. We further demonstrate how sentiments extracted from Twitter can be utilized to improve the forecasting power of social media.
1,909 citations
Book•
04 May 2009
TL;DR: In this paper, the authors discuss YouTube and the mainstream media and discuss YouTube's cultural politics, and YouTube's Uncertain Future and Popular Culture, as well as YouTube's Social Network.
Abstract: Acknowledgements. Preface. 1. How YouTube Matters. 2. YouTube and the Mainstream Media. 3. YouTube's Popular Culture. 4. YouTube's Social Network. 5. YouTube's Cultural Politics. 6. YouTube's Uncertain Futures. What Happened Before YouTube (Henry Jenkins). Uses of YouTube: Digital Literacy and the Growth of Knowledge (John Hartley)
833 citations
416 citations
TL;DR: Comparison of the use of the neural network in predicting the financial performance of a movie at the box-office before its theatrical release to models proposed in the recent literature as well as other statistical techniques using a 10-fold cross validation methodology shows that the neural networks do a much better job of predicting.
Abstract: Predicting box-office receipts of a particular motion picture has intrigued many scholars and industry leaders as a difficult and challenging problem. In this study, the use of neural networks in predicting the financial performance of a movie at the box-office before its theatrical release is explored. In our model, the forecasting problem is converted into a classification problem-rather than forecasting the point estimate of box-office receipts, a movie based on its box-office receipts in one of nine categories is classified, ranging from a 'flop' to a 'blockbuster.' Because our model is designed to predict the expected revenue range of a movie before its theatrical release, it can be used as a powerful decision aid by studios, distributors, and exhibitors. Our prediction results is presented using two performance measures: average percent success rate of classifying a movie's success exactly, or within one class of its actual performance. Comparison of our neural network to models proposed in the recent literature as well as other statistical techniques using a 10-fold cross validation methodology shows that the neural networks do a much better job of predicting in this setting.
291 citations
TL;DR: In this study, a multi-layer BP neural network with multi-input and multi-output is employed to build the prediction model and the comparison results with the MLP method show that the MLBP prediction model achieves more satisfactory results, and it is more reliable and effective to solve the problem.
Abstract: Forecasting box office revenue of a movie before its theatrical release is a difficult and challenging problem. In this study, a multi-layer BP neural network (MLBP) with multi-input and multi-output is employed to build the prediction model. All the movies are divided into six categories ranged from ''blob'' to ''bomb'' according to their box office incomes, and the purpose is to predict a film into the right class. The selections of the input variables are based on market survey and their weight values are determined by using statistical method. As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers. Then a classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level. Finally, a 6-fold cross-validation experiment methodology is used to measure the performance of the prediction model. The comparison results with the MLP method show that the MLBP prediction model achieves more satisfactory results, and it is more reliable and effective to solve the problem.
125 citations