13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews
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Anomitro Das
Abstract
Sentiment analysis is a common task in natural language processing that aims to detect the polarity of a text document. In the simplest situation, we distinguish only between positive and negative sentiment, turning the task into a standard binary classification problem. Sentiment classification can be useful in business intelligence by quickly summarizing consumer sentiment as feedback for a particular product. Movie reviews are of great importance as they can help viewers get an overview of the movie and also give producers and directors feedback on their work based on the public’s opinion. However, manually analyzing the sentiments in reviews becomes tedious due to the large amount of corpuses present in multiple movie review sites. In this project, we investigate various deep learning algorithms for sentiment classification and try to implement different ensemble models over three sequential models on the IMDb movie review dataset. The results of this research show that the ensemble models achieve higher accuracy than their base learners, the highest being 90.4 % on the IMDb dataset, which is on par with state-of-the-art research and our proposed model. These results demonstrate that ensemble learning methods can be used as a viable method for sentiment classification. Our results are easily reproducible, as we publish the code/notebooks of our experiment.
Abstract
Sentiment analysis is a common task in natural language processing that aims to detect the polarity of a text document. In the simplest situation, we distinguish only between positive and negative sentiment, turning the task into a standard binary classification problem. Sentiment classification can be useful in business intelligence by quickly summarizing consumer sentiment as feedback for a particular product. Movie reviews are of great importance as they can help viewers get an overview of the movie and also give producers and directors feedback on their work based on the public’s opinion. However, manually analyzing the sentiments in reviews becomes tedious due to the large amount of corpuses present in multiple movie review sites. In this project, we investigate various deep learning algorithms for sentiment classification and try to implement different ensemble models over three sequential models on the IMDb movie review dataset. The results of this research show that the ensemble models achieve higher accuracy than their base learners, the highest being 90.4 % on the IMDb dataset, which is on par with state-of-the-art research and our proposed model. These results demonstrate that ensemble learning methods can be used as a viable method for sentiment classification. Our results are easily reproducible, as we publish the code/notebooks of our experiment.
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
