Text Mining and Sentiment Analysis for Predicting Box Office Success

doi:10.3837/tiis.2018.08.030

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…We will use Twitter as a reference of public opinion. The justification for choosing to work with Twitter is because it has a very large social network [10] and has proven its ability to predict very difficult phenomena such as stock market prediction [11], movies revenue [12], and election results [13]. Using Twitter application programming interface (API), we have collected 30024 valid tweets about vaccination from 21/03/2022 to 29/03/2022 that were published in India in the English language.…”

Section: Data Collection 311 Corpus1mentioning

confidence: 99%

Online panel data quality: a sentiment analysis based on a deep learning approach

Ibtissam

Abdallah

Mohamed³

2023

IJ-AI

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The rise of online access panels has profoundly changed the market research landscape. Often presented by their owners as very powerful tools, they nevertheless raise important scientific questions, particularly regarding the representativeness of the samples they produce and, consequently, the validity of the information they provide. In this paper, we present an innovative approach, based on deep learning and sentiment analysis techniques, to assess in real time the representativeness of an online panel sample. The idea is to measure the extent to which the opinions of an online panel converge with opinions on social networks. To validate the proposed method, we conducted a case study on the emerging discussion on Coronavirus disease (COVID-19) vaccination. The results not only proved the representativeness of online panel sample, but also demonstrated the feasibility and effectiveness of our approach.

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Section: Data Collection 311 Corpus1mentioning

confidence: 99%

Online panel data quality: a sentiment analysis based on a deep learning approach

Ibtissam

Abdallah

Mohamed³

2023

IJ-AI

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“…The success of the box office films on a movie streaming device can be predicted [19] using text mining and sentiment analysis with the SVM model, producing a classification accuracy of 81.69%. Additionally, other research [20] outlined the method of disclosing user attitudes on certain dimensions of the film to obtain a rating review expressed by moviegoers.…”

Section: Sentiment Analysis Of Film Ratingsmentioning

confidence: 99%

The Comparison of Sentiment Analysis of Moon Knight Movie Reviews between Multinomial Naive Bayes and Support Vector Machine

Ajhari

2023

Appl. Inf. Syst. Manage.

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Online movie streaming platforms have changed the current pattern of watching movies. Besides providing convenience in watching anywhere and anytime, this service is provided at a lower cost to moviegoers. The increase in moviegoers on online streaming platforms has resulted in easy-to-find reviews. This review can determine whether they decide to watch the film or not. The moviegoers' reviews can be easily and quickly found for analysis using sentiment analysis to find a film's worthiness. This study used sentiment analysis in classifying Twitter data predictions using the Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM). In the sentiment analysis of labeling with positive and negative categories, a distilled version of BERT (DistilBERT) was used in this study. With a little human assistance in preprocessing, the model worked objectively with an overall accuracy performance on the confusion matrix of 64.50% for the Multinomial Naive Bayes model and 64.12% for the Support Vector Machine model. Performance evaluation was also carried out by calculating the cross-validation accuracy, which resulted in an accuracy of 72.38% for the MNB. Meanwhile, the SVM model obtained an accuracy of 70.19%.

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“…The most important costs are related to production factors. However, the title of a movie is one of the relatively inexpensive production factors that studios can use ( Kim et al, 2018 ; Liu and Xie, 2019 ). Bae and Kim (2019) examined an informative movie title, that is, a movie title containing movie genre or storyline information.…”

Section: Introductionmentioning

confidence: 99%

Mining of Movie Box Office and Movie Review Topics Using Social Network Big Data

Chen

Zhe

2022

Front. Psychol.

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In order to solve the problems of high investment and low box office losses in the film industry, this study analyzes the topic of film box office and film and television reviews based on social network big data. Firstly, the factors that affect the box office of the movie are analyzed. Secondly, continuous and discrete feature parts, text parts, and fusion parts are merged. The box office prediction model of mixed features using deep learning is established, and the movie box office is predicted. Finally, compared with other algorithms and models, the box office prediction model of mixed features using deep learning is verified. The results show that compared with other models, the prediction accuracy of the mixed feature movie box office prediction model using depthwise separable convolution (DSC)-Transformer is higher than that of other algorithm models. Its optimal mean square error (MSE) value is 0.6549, and the optimal mean absolute error (MAE) value is 0.1706. The constructed model predicts the box office of nine movies, and the error between the predicted value and the true value is about 10%. Therefore, the established movie box office prediction model has a good effect. This study can predict movies’ box office to reduce investment risk, so it is of great significance to movie investors and the social economy.

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Text Mining and Sentiment Analysis for Predicting Box Office Success

Cited by 9 publications

References 17 publications

Online panel data quality: a sentiment analysis based on a deep learning approach

Online panel data quality: a sentiment analysis based on a deep learning approach

The Comparison of Sentiment Analysis of Moon Knight Movie Reviews between Multinomial Naive Bayes and Support Vector Machine

Mining of Movie Box Office and Movie Review Topics Using Social Network Big Data

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