Bollywood Movie Success Prediction using Machine Learning Algorithms

Kanitkar, Ashutosh

doi:10.1109/cimca.2018.8739693

Cited by 10 publications

(3 citation statements)

References 1 publication

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“…Genre and sequel will be considered for future work. Kanitkar [29] prognosticated the Box office success of Indian movies using KNN, DT, NB, RF, and NN. Only a dataset of 250 MRs is taken.…”

Section: Related Literaturementioning

confidence: 99%

Toward Machine Learning Based Binary Sentiment Classification of Movie Reviews for Resource Restraint Language (RRL)—Hindi

Sharma

Ghose

2023

IEEE Access

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Sentiment analysis has significantly progressed in English, whereas Hindi research is still nascent. Despite being the third most spoken language worldwide, Hindi remains an RRL. Movie reviews are a treasure trove of opinionated content fueled by people's passionate engagement with film industry. The proliferation of great use of Hindi in writing reviews has catalyzed our endeavor to devise an approach for bipolar sentiment classification of movie reviews. We compiled and manually annotated a Hindi Language Movie Review (HLMR) dataset comprising 10K reviews for experiments, and challenges associated with Hindi have also been identified. In addition to HLMR, two publicly available IIT-P movie and product review datasets are used. Following dataset preprocessing, we explored TF-ISF with word-level N-gram features for text representation. Studies suggest that performance of machine learning approaches can be enhanced by hyperparameter tuning and ensemble learning. Several baseline classifiers were initially applied, and their parameters were hyper-tuned using Grid search. Subsequently, ensemble-based classifiers were applied individually. Lastly, we propose a simplistic yet powerful stacked ensemble-based architecture (SEBA), which effectively classifies Hindi reviews by leveraging the strengths of both approaches. Comprehensive experiments were conducted on all deployed datasets. Empirical results demonstrate that SEBA outperformed individual baselines and exhibited superior performance with unigrams and TF-ISF as features across deployed datasets. SEBA achieved an accuracy, precision, and recall of 0.808% and an F1-score of 0.807% on the HLMR dataset. These findings strongly advocate for the effectiveness of proposed solution and indicate its suitability for online deployment in binary review classification tasks.

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Section: Related Literaturementioning

confidence: 99%

Toward Machine Learning Based Binary Sentiment Classification of Movie Reviews for Resource Restraint Language (RRL)—Hindi

Sharma

Ghose

2023

IEEE Access

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“…This paper [11] has illustrated the use of different regression models, neural networks and classification algorithms to predict the success of Bollywood movies using data from Wikipedia, RadioMirchi and BoxOfficeIndia. The data is normalized before feeding into models.…”

Section: Literature Reviewmentioning

confidence: 99%

Movie Rating Prediction using Ensemble Learning Algorithms

Mhowwala¹,

Sulthana²,

Shetty³

2020

IJACSA

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Over the last few decades, social media platforms have gained a lot of popularity. People of all ages, gender, and areas of life have their presence on at least one of the social platforms. The data that is generated on these platforms has been and is being used for better recommendations, marketing activities, forecasting, and predictions. Considering predictions, the movie industry worldwide produces a large number of movies per year. The success of these movies depends on various factors like budget, director, actor, etc. However, it has become a trend to predict the rating of the movie based on the data collected from social media related to the movie. This will help a number of businesses relying on the movie industry in making promotional and marketing decisions. In this report, the aim is to collect movie data from IMDB and its social media data from YouTube and Wikipedia and compare the performance of two machine learning algorithms-Random Forest and XGBoostbest known for their high accuracy with small datasets, but large feature set. The collection of data is done from multiple sources or APIs.

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“…Few previous work portend gross of a movie depending on stochastic and regression models by using IMDb data. Some of them categorized either success or flop based on their revenues and apply binary classifications [3] for forecast. The measure of success of a movie does not completely depend on revenue.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Movie Performance using Machine Learning Algorithms

Pawar¹

2020

IJRASET

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Movies play an important role in our everyday life. Film industry involves huge investment in terms of money, time, efforts and people. The number of movies produced in the world is growing at an exponential rate and success rate of movie is of utmost importance since billions of dollars are invested in the making of each of these movies. In such a scenario, prior knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning, which itself is an expensive affair altogether. So, the prediction of the success of a movie is very essential to the film industry. In this proposed system, we give our detailed analysis of the Internet Movie Database (IMDb) and predict the IMDb score. This database contains categorical and numerical information such as IMDb score, director, gross, budget and so on and so forth. This system proposes a way to predict how successful a movie will be prior to its arrival at the box office instead of listening to critics and others onwhether a movie will be successful or not. The proposed system provides a quite efficient approach to predict IMDb score on IMDb Movie Dataset. We will try to unveil the important factors influencing the score of IMDb Movie Data. In the exploratory analysis we found that number of voted users, number of critics for reviews, number of Facebook likes, duration of the movie and gross collection of movie affect the IMDb score strongly. Drama and Biopic movies are best in genres.

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Bollywood Movie Success Prediction using Machine Learning Algorithms

Cited by 10 publications

References 1 publication

Toward Machine Learning Based Binary Sentiment Classification of Movie Reviews for Resource Restraint Language (RRL)—Hindi

Toward Machine Learning Based Binary Sentiment Classification of Movie Reviews for Resource Restraint Language (RRL)—Hindi

Movie Rating Prediction using Ensemble Learning Algorithms

Prediction of Movie Performance using Machine Learning Algorithms

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