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Machine learning and deep learning-based sentiment analysis of IMDB user reviews
- 1 Lanzhou University
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Abstract
Movie reviews have always been a popular and enduring subject of interest among researchers. Sentiment analysis plays a significant role in this domain. The utilization of machine learning and natural language processing techniques can provide valuable insights into the emotional responses of audiences towards movies, as well as facilitate the appraisal of their reputation and market potential. This is achieved through the analysis of sentiment expressed in movie reviews. Furthermore, this approach is highly valuable in various application domains such as data mining, web mining, and social media analysis. This paper aims to conduct a comparative analysis by utilizing typical models based on machine learning and neural networks, along with the integration of natural language processing techniques. The IMDB database, which contains 50,000 reviews, will be used, and data preprocessing will be performed before applying these models. By comparing the accuracy of each model, insights regarding movie reviews can be derived.
Keywords
Machine learning, Encoder-Decoder, sentiment analysis, movies reviews, binary classification
[1]. Yenter A and Verma A 2017 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conf. (UEMCON) 540-546
[2]. Zhang X, Zhao J and LeCun Y 2015 Advances in Neural Information Processing Systems 28 649-658
[3]. Feldman R 2013 Communications of the ACM 56 4 82-9
[4]. Ahmad SR, Bakar AA and Yaakub MR 2019 Intelligent data analysis 23 1 159-89
[5]. Koto F and Adriani M 2015 Natural Language Processing and Information Systems 9103 453-457
[6]. Nielsen F A 2011 arXiv preprint arXiv 1103 2903
[7]. Thelwall M, Buckley K and Paltoglou G 2012 Journal of the American Society for Information Science and Technology 63 1 163-73
[8]. Taboada M 2016 Annual Review of Linguistics 2 325-75
[9]. Schouten K and Frasincar F 2015 IEEE transactions on knowledge and data engineering 28 3 813-30
[10]. Medhat W, Hassan A and Korashy H 2014 Ain Shams engineering journal 5 4 1093-113
[11]. Ravi K and Ravi V 2019 Knowledge-based systems 89 14-46
[12]. Prabha MI and Umarani Srikanth G 2019 2019 1st Int. Conf. on Innovations in Information and Communication Technology (ICIICT) 1-9
[13]. Ain QT, Ali M, Riaz A, Noureen A, Kamran M, Hayat B and Rehman A 2017 International Journal of Advanced Computer Science and Applications 8 6
[14]. Zhang L, Wang S and Liu B 2018 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8 4 e1253
[15]. Habimana O, Li Y, Li R, Gu X and Yu G Science China Information Sciences 63 1-36
[16]. Church KW 2017 Natural Language Engineering 23 1 155-62
Cite this article
Xia,S. (2024). Machine learning and deep learning-based sentiment analysis of IMDB user reviews. Applied and Computational Engineering,53,113-125.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
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