An Integrated Hybridization Framework of Machine Learning and Deep Neural Architectures for Robust Textual Sentiment Classification in Movie Review Analytics

DOI

10.2991/978-94-6239-674-6_35How to use a DOI?

Keywords

Sentiment Analysis; Hybrid Machine Learning; Deep Neural Networks; Movie Reviews; Text Analytics; Robust Classification; Contextual Embeddings; Simulation-Based Evaluation

Abstract

The research reported on this paper developed an integrated hybrid model using machine learning (ML) and deep neural networks (DNNs) for developing accurate sentiment classification techniques for movie reviews. This hybrid architecture combined classical ML algorithms with Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and transformer-based embedding techniques to extract both shallow and deep level information from movie review texts. Experiments were run to test the performance of the hybrid model under varying amounts of noise within the data, different embedding techniques used, and weight values used in the fusion of results, thereby providing a realistic and applicable evaluation. The hybrid model consistently performed better than the individual ML and DNN models regarding accuracy, macro F1 score, and robustness; also maintained a balance between computational efficiency and performance. Calibration and Pareto front analysis provided evidence of the reliability of the proposed framework and showed the trade- offs involved. As such, the proposed framework has provided a scalable, interpretable, and highly effective method of performing sentiment analysis in the real world.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Praveen Sharma
AU  - Divyarth Rai
PY  - 2026
DA  - 2026/05/28
TI  - An Integrated Hybridization Framework of Machine Learning and Deep Neural Architectures for Robust Textual Sentiment Classification in Movie Review Analytics
BT  - Proceedings of the International Conference on Sustainable Computing and Artificial Intelligence (ICSCAI 2025)
PB  - Atlantis Press
SP  - 417
EP  - 430
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-674-6_35
DO  - 10.2991/978-94-6239-674-6_35
ID  - Sharma2026
ER  -