Early-production stage prediction of movies success using K-fold hybrid deep ensemble learning model

Sahu, Sandipan; Kumar, Raghvendra; Long, Hoàng Việt; Shafi, Pathan Mohd

doi:10.1007/s11042-022-13448-0

Cited by 8 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most studies conducted in cinema have focused on predicting the success and profitability of movies through different data mining or machine learning methods. [9][10][11][12][13][14][15]. Among the scarce studies that have directed their attention toward the content of cinematic works, one has considered references to a particular movie as the basis for generating a graph.…”

Section: Literature Reviewmentioning

confidence: 99%

In the Arena of the Content War: A Social Network Analysis Approach for Content Differentiation in VOD Platforms

Noroozian,

Amiri,

Ramezani

2024

IEEE Access

View full text Add to dashboard Cite

The quest for content differentiation and audience engagement is paramount in the competitive landscape of Video-on-Demand (VOD) platforms. This paper proposes a system that could revolutionize how VOD platforms approach content creation. In this context, content differentiation refers to creating unique and distinct content that stands out from the competition. By investigating VOD platform series data, the system can recommend actors and themes to VOD vendors, helping them create innovative storylines. To achieve this, we analyzed the storylines of 72 VOD movie series to extract keywords. Using these keywords, we generated a network using Social Network Analysis. By examining the network, we identified content gaps. After semantic clustering, we utilized TOPSIS to determine keywords with high centrality scores. These are a foundation for connecting peripheral keywords and generating new content. The paper's findings suggest that the proposed content-based recommender system can help VOD platforms create innovative storylines by leveraging structural holes in a TV series content keyword graph. The study also suggests successful directors take risks by combining keywords from various network parts. This approach allows them to attract a broader range of audiences and increase their chances of success.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

In the Arena of the Content War: A Social Network Analysis Approach for Content Differentiation in VOD Platforms

Noroozian,

Amiri,

Ramezani

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Wikipedia provided the textual summary that they obtained. For predicting "successful," they received a F1 score of 0.68, while for predicting "unsuccessful," they received a score of 0.70.In 2022 Sandipan Sahu and group published a paper which used K-fold hybrid deep ensemble learning model to predict success of a movie in early-production stage [4]. They have collected the past 30 years data regarding of Indian movie information, especially all regional wise movies and their proposed model delivered 96% accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

Explainable Neural Network analysis on Movie Success Prediction

Bhavesh Kumar,

Pande

2023

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

These days movies are one of the most important part of entertainment industry and back in the days you could see everyday people standing outside theatres, or watching movies in OTT platforms. But due to busy schedules not many people are watching every movie. They go over the internet and search for top rated movies and go to theatres. And creating a successful movie is no easy job. Thus, this study helps movie producers to consider what are the important factors that influence a movie to be successful. this study applied neural network model to the IMDb dataset and then due to its complex nature in order to achieve the local explainability and global explainability for the enhanced analysis, study have used SHAP (Shapley additive explanations) to analysis.

show abstract

Enhancing Mutual Fund Price Prediction: A Hybrid Ensemble Approach with Random Forest, SVR, Ridge, and Gradient Boosting Regressors

Kumar,

Srivastava,

Prakash

2024

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Early-production stage prediction of movies success using K-fold hybrid deep ensemble learning model

Cited by 8 publications

References 41 publications

In the Arena of the Content War: A Social Network Analysis Approach for Content Differentiation in VOD Platforms

In the Arena of the Content War: A Social Network Analysis Approach for Content Differentiation in VOD Platforms

Explainable Neural Network analysis on Movie Success Prediction

Enhancing Mutual Fund Price Prediction: A Hybrid Ensemble Approach with Random Forest, SVR, Ridge, and Gradient Boosting Regressors

Contact Info

Product

Resources

About