ArticleComputational intelligence and neuroscience2021
Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 102 citations in OpenAlex.
- A bimodal longitudinal investigation on changes in sentiments over social media interactions owing to COVID-19 pandemic.Scientific reports · 2025Article
- Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines.Sensors (Basel, Switzerland) · 2025Article
- Predicting an EEG-Based hypnotic time estimation with non-linear kernels of support vector machine algorithm.Cognitive neurodynamics · 2024Article
- A Multimodal Approach for Detection and Assessment of Depression Using Text, Audio and Video.Phenomics (Cham, Switzerland) · 2024Article
- Article
- Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia.Scientific reports · 2023Article
- 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
- Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention.Sensors (Basel, Switzerland) · 2023Article
- Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT Images.Computational intelligence and neuroscience · 2023Article
- Intelligent lead-based bidirectional long short term memory for COVID-19 sentiment analysis.Social network analysis and mining · 2023Article
- Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.Quantitative imaging in medicine and surgery · 2023Article
- The relationship between protein modified folding molecular network and Alzheimer's disease pathogenesis based on BAG2-HSC70-STUB1-MAPT expression patterns analysis.Frontiers in aging 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
- Article
- Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI.Computers in biology and medicine · 2022Article
- Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.Journal of medical systems · 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
- Semantic Sentiment Classification for COVID-19 Tweets Using Universal Sentence Encoder.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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 has claimed several human lives to this date. People are dying not only because of physical infection of the virus but also because of mental illness, which is linked to people's sentiments and psychologies. People's written texts/posts scattered on the web could help understand their psychology and the state they are in during this pandemic. In this paper, we analyze people's sentiment based on the classification of tweets collected from the social media platform, Twitter, in Nepal. For this, we, first, propose to use three different feature extraction methods-fastText-based (ft), domain-specific (ds), and domain-agnostic (da)-for the representation of tweets. Among these three methods, two methods ("ds" and "da") are the novel methods used in this study. Second, we propose three different convolution neural networks (CNNs) to implement the proposed features. Last, we ensemble such three CNNs models using ensemble CNN, which works in an end-to-end manner, to achieve the end results. For the evaluation of the proposed feature extraction methods and CNN models, we prepare a Nepali Twitter sentiment dataset, called NepCOV19Tweets, with 3 classes (positive, neutral, and negative). The experimental results on such dataset show that our proposed feature extraction methods possess the discriminating characteristics for the sentiment classification. Moreover, the proposed CNN models impart robust and stable performance on the proposed features. Also, our dataset can be used as a benchmark to study the COVID-19-related sentiment analysis in the Nepali language.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.