Academic Journal
Hybrid Deep Learning Models for Sentiment Analysis
| العنوان: | Hybrid Deep Learning Models for Sentiment Analysis |
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| المؤلفون: | Cach N. Dang, María N. Moreno-García, Fernando De la Prieta |
| المصدر: | Complexity, Vol 2021 (2021) |
| بيانات النشر: | Hindawi-Wiley, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test the reliability of several hybrid techniques on various datasets of different domains. Our research questions are aimed at determining whether it is possible to produce hybrid models that outperform single models with different domains and types of datasets. Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. The hybrid models are compared against three single models, SVM, LSTM, and CNN. Both reliability and computation time were considered in the evaluation of each technique. The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM. The reliability of the latter was significantly higher. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1076-2787 1099-0526 |
| Relation: | https://doaj.org/toc/1076-2787; https://doaj.org/toc/1099-0526 |
| DOI: | 10.1155/2021/9986920 |
| URL الوصول: | https://doaj.org/article/b5036af86f694f4897606117685f0017 |
| رقم الانضمام: | edsdoj.b5036af86f694f4897606117685f0017 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 10762787 10990526 |
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| DOI: | 10.1155/2021/9986920 |