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An intelligent box office predictor based on aspect-level sentiment analysis of movie review

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Abstract

Box office is a challenging and crucial task for the movie distributors in decision making. In recent years, movie reviews are widely posted and shared on intelligent multimedia systems and everywhere. In this work, we employ both the metadata of the movie and the sentiment information of the users’ reviews to establish an intelligent predicting model. In the sentiment polarity classification model, a co-attention network-based aspect-level sentiment analysis strategy is developed by using the specific word embedding representations from both the contexts and the aspect. Considering the movie success prediction, a Softmax Discriminant Classifier is used due to its capable of dealing with non-linear issues. The sentiments from review texts, together with the movie information are taken as input variables of the predictor. Experimental outcomes verify the working performance of the proposed method which indicates that our model can be further applied to the sentiment analysis and the predicting of movie success.

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Acknowledgements

This work was supported by Special Fund for the doctoral Scientific Research Fund of Guan** University of Science and Technolgoy (No.22Z08), Guan** University of Science and Technology with Liuzhou enterprise research project (BSGZ2218), and Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province (420S47).

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Authors and Affiliations

  1. Department of Computer, Hunan City University, Yiyang, 413000, China

    Gelan Yang

  2. School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, 545006, China

    Yiyi Xu

  3. College of Mechanical Electrical Engineering, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, 528400, China

    Li Tu

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  1. Gelan Yang
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  2. Yiyi Xu
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  3. Li Tu
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Correspondence to Yiyi Xu.

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Yang, G., Xu, Y. & Tu, L. An intelligent box office predictor based on aspect-level sentiment analysis of movie review. Wireless Netw 29, 3039–3049 (2023). https://doi.org/10.1007/s11276-023-03378-6

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