ArticleComputational intelligence and neuroscience2022
A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost.PeerJ. Computer science · 2025Article
- COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets.International journal of environmental research and public health · 2023Article
- Social media-based COVID-19 sentiment classification model using Bi-LSTM.Expert systems with applications · 2023Article
- Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.Quantitative imaging in medicine and surgery · 2023Article
- Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT Images.Computational intelligence and neuroscience · 2023Article
- Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada.Frontiers in digital health · 2023Article
- Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.Journal of medical systems · 2022Article
- Sentiment Analysis: An ERNIE-BiLSTM Approach to Bullet Screen Comments.Sensors (Basel, Switzerland) · 2022Article
- Deep Neural Networks Applied to Stock Market Sentiment Analysis.Sensors (Basel, Switzerland) · 2022Article
- Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis.Sensors (Basel, Switzerland) · 2022Article
- Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies.Healthcare (Basel, Switzerland) · 2022Article
- COVID-19 Outbreak Forecasting Based on Vaccine Rates and Tweets Classification.Computational intelligence and neuroscience · 2022Article
- Detecting Personal Medication Intake in Twitter via Domain Attention-Based RNN with Multi-Level Features.Computational intelligence and neuroscience · 2022Article
- Semantic Sentiment Classification for COVID-19 Tweets Using Universal Sentence Encoder.Computational intelligence and neuroscience · 2022Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples' death is not only linked to its infection but also to peoples' mental states and sentiments triggered by the fear of the virus. People's sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples' sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tree classifier, AdaBoost, and Multilayer Perceptron), based on the three feature representation methods: TF-IDF, FastText, and Hybrid. To evaluate our methods, we use a publicly available Nepali-COVID-19 tweets dataset, NepCov19Tweets, which consists of Nepali tweets categorized into three classes (Positive, Negative, and Neutral). The evaluation results on the NepCOV19Tweets show that the hybrid feature extraction method not only outperforms the other two individual feature extraction methods while using nine different machine learning algorithms but also provides excellent performance when compared with the state-of-the-art methods.
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