Sentiment Classification of Movie Reviews Based on the Ensemble Machine Learning Model

DOI

10.2991/978-94-6463-300-9_31How to use a DOI?

Keywords

Machine Learning; Ensemble Model; Artificial Neural Network

Abstract

Film reviews play a pivotal role in influencing audience decisions, necessitating accurate classification of sentiments as positive or negative, which holds significant importance for the film industry. To address this, the present study introduces an innovative ensemble learning approach that integrates artificial neural networks, LightGBM, and logistic regression models through a stacking technique. The ensemble model is empirically examined using the IMDB dataset, with a comparative analysis conducted against an individual Artificial Neural Network (ANN) model. The findings demonstrate remarkable enhancements, particularly in terms of accuracy and other relevant metrics, achieved by the ensemble model compared to the individual ANN model, specifically yielding an increase in accuracy from 0.8791 to 0.8904. This substantiates the substantial improvement in accuracy offered by the ensemble model, thereby underscoring the efficacy and potential of ensemble learning for sentiment classification in movie reviews. Moreover, an analysis of the confusion matrix reveals that the ensemble model predominantly improves the classification of reviews labeled as ‘positive,’ as evidenced by an increase in true positive instances from 4359 to 4451, accompanied by a decrease in false positive instances from 668 to 576. By amalgamating predictions from distinct models, the ensemble model effectively mitigates the limitations inherent to individual models and attains superior performance compared to relying on a single model alone.

Copyright

© 2023 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  - Zicheng Gan
PY  - 2023
DA  - 2023/11/27
TI  - Sentiment Classification of Movie Reviews Based on the Ensemble Machine Learning Model
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 307
EP  - 313
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_31
DO  - 10.2991/978-94-6463-300-9_31
ID  - Gan2023
ER  -