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Works279 from public data
- COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis345
This study presents a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020 and supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic.
- Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis298
This paper presents D I C E T, a transformer-based method for sentiment analysis that encodes representation from a transformer and applies deep intelligent contextual embedding to enhance the quality of tweets by removing noise while taking word sentiments, polysemy, syntax, and semantic knowledge into account.
- A Comprehensive Survey on Word Representation Models: From Classical to State-of-the-Art Word Representation Language Models203
A variety of text representation methods, and model designs have blossomed in the context of NLP, including SOTA LMs are described, which can transform large volumes of text into effective vector representations capturing the same semantic information.
- Conventional to online education during COVID-19 pandemic: Do develop and underdeveloped nations cope alike173
This study compared access & use of online learning among Bruneians and Pakistanis and found Bruneian are more satisfied with online learning as compared to Pakistanis.
- An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers129
The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy, which is of great importance to the medical industry and relevant research community.
- A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter120
This paper analyzes twelve different pre- processing techniques on three pre-classified Twitter datasets on hate speech and proposes a systematic approach to text pre-processing to apply differentPre-processing techniques in order to retain features without information loss.
- Early Identification of Depression Severity Levels on Reddit Using Ordinal Classification107
This paper reformulates the early identification of depression as an ordinal classification task over clinical depression standards such as Beck’s Depression Inventory and the Depressive Disorder Annotation scheme to identify depression severity levels and proposes a hierarchical attention method optimized to factor in the increasing depression severity Levels through a soft probability distribution.
- Breast cancer risk prediction using machine learning: a systematic review90
This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI, and offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.
- A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach88
This study implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies and showed an accuracy of 97% and predicted the top 1000 ratings for movies.
- Large language models (LLM) in computational social science: prospects, current state, and challenges84
A solid background on the potential of LLMs in CSS, their past applications, current problems, and how they can pave the way for revolutionizing CSS is provided.
- BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition82
The proposed biomedical ALBERT (A Lite Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) is an effective domain-specific pre-trained language model trained on a huge biomedical corpus designed to capture biomedical context-dependent NER.
- Prompting Large Language Models for Topic Modeling72
This paper proposes PromptTopic, a novel topic modeling approach that harnesses the advanced language understanding of large language models (LLMs) to address challenges of short text datasets that lack co-occurring words.
- The role of unmanned aerial vehicles and mmWave in 5G: Recent advances and challenges70
A consolidated synthesis on the role of UAVs and mmWave in 5G, emphasis on recent developments and challenges, and the scope of artificial intelligence and machine learning techniques as an efficient solution for combating the dynamic and complex nature of U AV‐based cellular communication networks are discussed.
- Adaption of distance learning to continue the academic year amid COVID-19 lockdown69
The findings indicate that trust in information sources such as institute and media information or interpersonal communication related to distance learning programs is correlated with awareness and readiness, and that readiness strongly influences the adoption of distance learning amid the COVID-19 pandemic.
- Benchmarking for biomedical natural language processing tasks with a domain specific ALBERT67
8 variants of BioALberT, a domain-specific adaptation of a lite bidirectional encoder representations from transformers (ALBERT), trained on biomedical and clinical corpora and fine-tuned for 6 different tasks across 20 benchmark datasets are presented.
- A Comparative Analysis of Active Learning for Biomedical Text Mining64
Experiments show that AL has the potential to significantly reducing the cost of manual labelling, and pre-labelling performed using AL expediates the labelling process by reducing the time required for labelling.
- Enhancing textual textbook question answering with large language models and retrieval augmented generation53
A framework (PLRTQA) is proposed that incorporates the retrieval augmented generation (RAG) technique to handle the out-of-domain scenario where concepts are spread across different lessons, and utilize transfer learning to handle the long context and enhance reasoning abilities.
- MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and Bilibili53
Evaluations of state-of-the-art video classification models, such as VLM, GPT-4V and Qwen-VL, on MultiHateClip highlight the existing challenges in accurately distinguishing between hateful and offensive content and the urgent need for models that are both multimodally and culturally nuanced.
- Performance Evaluation of Next-Generation Wireless (5G) UAV Relay53
This study investigates, analyzes and describes the distinctive rich characteristics of mmWave propagation in Access and backhaul network simultaneously using UAV, and highlights the impact of UAV location to maximize the performance of an Amplify-and-Forward UAV based relay for providing enhanced coverage to the users.
- MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network52
MoCFL enhances feature aggregation by introducing an affinity matrix that quantifies the similarity between local feature extractors from different clients, addressing dynamic data distribution changes caused by frequent client churn and topology changes.
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- Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords51
The dual role of Large Language Models in the context of online misinformation and disinformation and their potential to generate and counter misinformation and disinformation in sensitive topics such as healthcare, COVID-19, and political agendas are explored.
- Identifying Creative Harmful Memes via Prompt based Approach48
This paper reformulates a harmful meme analysis as an auto-filling and presents a prompt-based approach to identify harmful memes, which outperformed state-of-the-art methods on two benchmark harmful memes datasets.
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- A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection45
A multi-criteria method to empirically assess and rank classifiers for Arabic sentiment analysis, which suggests that deep learning and support vector machine (SVM) classifiers perform best and outperform decision trees, K-nearest neighbours, and Naïve Bayes classifiers.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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