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Optimal hierarchical attention network-based sentiment analysis for movie recommendation

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Abstract

In recent days, recommender systems are extremely widespread applications for handling information overload issues by offering personalized recommendations, and also, the researchers began to utilize the sentiment analysis-based movie recommendation system. This paper devises a novel technique for sentimental classification, which helps to recommend the positive reviewed movies. Here, the water cycle earthworm Optimization (WCEWO) is newly devised by incorporating water cycle algorithm and earthworm optimization algorithm. The procedure is started by providing the input to the matrix calculation stage, which then builds the matrix based on the user's preferences. Here, the service grouping is performed using ROCK algorithm. Thereafter, the group matching is performed by Cosine similarity for achieving best group from all other groups. Once the group matching is done, the matching is carried out between the query and the visitor binary sequence using Kendall Tau distance measure for identifying best visitor sequence. Finally, sentimental classification is done using hierarchical attention networks (HAN), where the training process of HAN is performed using WCEWO algorithm and hence the suitable movie recommendation is achieved by offering appropriate positive reviewed movie recommendations to the users. Mean square error (MSE), root-mean-square error (RMSE), and accuracy are used as performance indicators to assess the effectiveness of the established approach. The created WCEWO-based HAN gives maximum accuracy of 89.81%, minimum MSE of 0.185, and minimum RMSE of 0.430.

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Data availability statement

Netflix movie recommendation dataset taken from, https://www.kaggle.com/laowingkin/netflix-movie-recommendation, accessed on March 2021. Large Movie review dataset taken from, http://ai.stanford.edu/~amaas/data/sentiment/, accessed on March 2021.

References

  • Alhijawi B, Kilani Y (2020) A collaborative filtering recommender system using genetic algorithm. Inf Process Manage 57(6):102310

    Article  Google Scholar 

  • Bansal T, Belanger D, McCallum A (2016) Ask the gru: multi-task learning for deep text recommendations. In: Proceedings of the 10th ACM conference on recommender systems pp 107–114

  • Batmaz Z, Yurekli A, Bilge A, Kaleli C (2019) A review on deep learning for recommender systems: challenges and remedies. Artif Intell Rev 52(1):1–37

    Article  Google Scholar 

  • Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132

    Article  Google Scholar 

  • Da’u A, Salim N (2019) Sentiment-aware deep recommender system with neural attention networks. IEEE Access 7:45472–84

    Article  Google Scholar 

  • Dhumane AV (2020) Examining user experience of elearning systems using EKhool learners. J Netw Commun Syst 3(4):26–34

    Google Scholar 

  • Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166

    Article  Google Scholar 

  • Gao J, Zhang T, Xu C (2017) A unified personalized video recommendation via dynamic recurrent neural networks. In: Proceedings of the 25th ACM international conference on Multimedia pp 127–135

  • Ghosh M, Kar A (2013) Unsupervised linguistic approach for sentiment classification from online reviews using SentiWordNet 3.0. Int J Eng Res Technol 2(9):1–6

    Google Scholar 

  • Guha S, Rastogi R, Shim K (2000) ROCK: a robust clustering algorithm for categorical attributes. Inf Syst 25(5):345–366

    Article  Google Scholar 

  • Hidasi B, Quadrana M, Karatzoglou A, Tikk D (2016) Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM conference on recommender systems pp 241–248

  • Iqbal M, Ghazanfar MA, Sattar A, Maqsood M, Khan S, Mehmood I, Baik SW (2019) Kernel context recommender system (KCR): a scalable context-aware recommender system algorithm. IEEE Access 7:24719–24737

    Article  Google Scholar 

  • Jin Z, Li Q, Zeng DD, Zhan Y, Liu R, Wang L, Ma H (2016) Jointly modeling review content and aspect ratings for review rating prediction. In: Proceedings of the 39th International ACM SIGIR conference on research and development in information retrieval pp 893–896

  • Jusup M, Holme P, Kanazawa K, Takayasu M, Romić I, Wang Z, Geček S, Lipić T, Podobnik B, Wang L, Luo W, Klanjšček T, Fan J, Boccaletti S, Perc M (2022) Social physics. Phys Rep 948:1–148

    Article  MathSciNet  Google Scholar 

  • Kavuri S, Kancherla GR, Brao B (2016) A novel hardware parameters based cloud data encryption and decryption against unauthorized users. J Theor Appl Inf Technol 87(2):291–299

    Google Scholar 

  • Kavuri SKSVA, Kancherla GR, Bobba B (2017) An improved integrated hash and attributed based encryption model on high dimensional data in cloud environment. Int J Electr Comput Eng 7(2):950–960

    Google Scholar 

  • Kim D, Park C, Oh J, Lee S, Yu H (2016) Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM conference on recommender systems pp 233–240

  • Large movie review dataset, http://ai.stanford.edu/~amaas/data/sentiment/, accessed on March 2021.

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  • Ling G, Lyu MR, King I (2014) Ratings meet reviews, a combined approach to recommend. In: Proceedings of the 8th ACM conference on recommender systems pp 105–112

  • Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32

    Article  Google Scholar 

  • McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on Recommender systems pp 165–172

  • Mohamad AY, Harun SR, Shahidan NAA, Nanthaamornphong A, Mustapha A, Wahab MHA (2022) Collaborative Filtering Approach for Movie Recommendations. In: The proceeding of 19th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), IEEE

  • Netflix movie recommendation dataset, https://www.kaggle.com/laowingkin/netflix-movie-recommendation, accessed on March 2021.

  • Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40(3):56–58

    Article  Google Scholar 

  • Sacenti JAP, Fileto R, Willrich R (2022) Knowledge graph summarization impacts on movie recommendations. J Intell Inf Syst 58:43–66

    Article  Google Scholar 

  • Shambour Q (2021) A deep learning based algorithm for multi-criteria recommender systems. Knowl Based Syst 211:106545

    Article  Google Scholar 

  • Shende DK, Angal Y, Sonavane SS (2020) A comprehensive survey of the routing schemes for IoT applications. Scalable Comput Pract Exp 21(2):203–216

    Article  Google Scholar 

  • Song Y, Elkahky AM, He X (2016) Multi-rate deep learning for temporal recommendation. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval pp 909–912

  • Tang D, Qin B, Wei F, Dong L, Liu T, Zhou M (2015) A joint segmentation and classification framework for sentence level sentiment classification. IEEE/ACM Trans Audio Speech Lang Process 23(11):1750–1761

    Article  Google Scholar 

  • Walek B, Fojtik V (2020) A hybrid recommender system for recommending relevant movies using an expert system. Expert Syst Appl 158:113452

    Article  Google Scholar 

  • Wang GG, Deb S, Coelho LD (2018) Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. Int J Bio Inspir Comput 12(1):1–22

    Article  Google Scholar 

  • Wang C, Niepert M, Li H (2019) Recsys-dan: discriminative adversarial networks for cross-domain recommender systems. IEEE Trans Neural Netw Learn Syst 31(8):2731–2740

    Article  Google Scholar 

  • Wu CY, Ahmed A, Beutel A, Smola AJ, Jing H (2017) Recurrent recommender networks. In: Proceedings of the tenth ACM international conference on web search and data mining pp 495–503

  • Yalcin E, Ismailoglu F, Bilge A (2021) An entropy empowered hybridized aggregation technique for group recommender systems. Expert Syst Appl 166:114111

    Article  Google Scholar 

  • Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies pp 1480–1489

  • Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1–38

    Article  Google Scholar 

  • Zhang F, Qi S, Liu Q, Mao M, Zeng A (2020) Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks. Expert Syst Appl 149:113346

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Assam Down Town University, Guwahati, India

    Deepjyoti Roy & Mala Dutta

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  1. Deepjyoti Roy
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  2. Mala Dutta
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Correspondence to Deepjyoti Roy.

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Roy, D., Dutta, M. Optimal hierarchical attention network-based sentiment analysis for movie recommendation. Soc. Netw. Anal. Min. 12, 138 (2022). https://doi.org/10.1007/s13278-022-00954-0

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  • DOI: https://doi.org/10.1007/s13278-022-00954-0

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