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MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
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In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
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| 2 |
Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian
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In: Electronics; Volume 11; Issue 3; Pages: 374 (2022)
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| 3 |
A New Ontology-Based Method for Arabic Sentiment Analysis
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In: Big Data and Cognitive Computing; Volume 6; Issue 2; Pages: 48 (2022)
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| 4 |
COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset
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In: Healthcare; Volume 10; Issue 3; Pages: 411 (2022)
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| 5 |
Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model
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In: Applied Sciences; Volume 12; Issue 2; Pages: 692 (2022)
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| 6 |
Analysis of Destination Images in the Emerging Ski Market: The Case Study in the Host City of the 2022 Beijing Winter Olympic Games
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In: Sustainability; Volume 14; Issue 1; Pages: 555 (2022)
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| 7 |
Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2707 (2022)
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Abstract:
This paper describes the construction a short-text aspect-based sentiment analysis method based on Convolutional Neural Network (CNN) and Bidirectional Gating Recurrent Unit (BiGRU). The hybrid model can fully extract text features, solve the problem of long-distance dependence on the sequence, and improve the reliability of training. This article reports empirical research conducted on the basis of literature research. The first step was to obtain the dataset and perform preprocessing, after which scikit-learn was used to perform TF-IDF calculations to obtain the feature word vector weight, obtain the aspect-level feature ontology words of the evaluated text, and manually mark the ontology of the reviewed text and the corresponding sentiment analysis polarity. In the sentiment analysis section, a hybrid model based on CNN and BiGRU (CNN + BiGRU) was constructed, which uses corpus sentences and feature words as the vector input and predicts the emotional polarity. The experimental results prove that the classification accuracy of the improved CNN + BiGRU model was improved by 12.12%, 8.37%, and 4.46% compared with the Convolutional Neural Network model (CNN), Long-Short Term Memory model (LSTM), and Convolutional Neural Network (C-LSTM) model.
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Keyword:
aspect-level; bidirectional gating recurrent unit (BiGRU); convolutional neural network (CNN); sentiment analysis; short text
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URL: https://doi.org/10.3390/app12052707
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| 8 |
Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches
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In: Sustainability; Volume 14; Issue 8; Pages: 4723 (2022)
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| 9 |
How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
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In: Sustainability; Volume 14; Issue 5; Pages: 2675 (2022)
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| 10 |
Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2694 (2022)
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| 11 |
How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 3; Pages: 1822 (2022)
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| 12 |
TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels
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In: Data; Volume 7; Issue 1; Pages: 8 (2022)
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| 13 |
Detecting Depression Signs on Social Media: A Systematic Literature Review
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In: Healthcare; Volume 10; Issue 2; Pages: 291 (2022)
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| 18 |
Using Campaign Communications to Analyze Civility in Ranked Choice Voting Elections
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In: Politics and Governance ; 9 ; 2 ; 280-292 ; The Politics, Promise and Peril of Ranked Choice Voting (2022)
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| 19 |
Tracing the Legitimacy of Artificial Intelligence – A Media Analysis, 1980-2020
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| 20 |
Understanding cyberslacking intention during Covid-19 online classes: An fsQCA analysis
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