Aspect-based Sentiment Analysis (ABSA) using Machine Learning Algorithms DOI
Ayesha Siddiqua,

V Bindumathi,

Ganesh Raghu

et al.

Published: April 26, 2024

Aspect-Based Sentiment Analysis (ABSA) is a Natural Language Processing task that aims to identify and extract the sentiment of specific aspects or components product service. ABSA typically involves multi-step process begins with identifying features service are being discussed in text. This followed by analysis, where polarity (positive, negative, neutral) assigned each aspect based on context sentence document. Finally, results aggregated provide an overall for aspect. The training machine learning models classify text neutral). First, we transform data using Term Frequency-Inverse Document Frequency (TF-IDF), which assigns weights words their importance within document collection. emphasizes informative terms. Then, these TF-IDF fed into both SVM Logistic Regression models. find hyper plane best separates classes, while calculates probability belonging class. Extensive experiments have been conducted datasets covid vaccinations dataset show support vector model achieves excellent performance terms extraction classification. Twitter can be imbalanced, more positive negative tweets depending topic. affect process. Techniques like oversampling undersampling minority class might necessary. work investigates algorithms classification task. Support Vector Machine (SVM) (LR) were compared. indicate achieved superior accuracy (87.34%) compared (84.64%), suggesting as suitable option this

Language: Английский

Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers DOI Creative Commons
Staphord Bengesi,

Hoda El-Sayed,

Md Kamruzzaman Sarker

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 69812 - 69837

Published: Jan. 1, 2024

Language: Английский

Citations

70
Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox DOI Creative Commons
Nirmalya Thakur

Big Data and Cognitive Computing, Journal Year: 2023, Volume and Issue: 7(2), P. 116 - 116

Published: June 9, 2023

Mining and analysis of the big data Twitter conversations have been significant interest to scientific community in fields healthcare, epidemiology, data, science, computer their related areas, as can be seen from several works last few years that focused on sentiment other forms text tweets Ebola, E-Coli, Dengue, Human Papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel kidney disease, lupus, Parkinson’s, Diphtheria, West Nile virus. The recent outbreaks COVID-19 MPox served “catalysts” for usage seeking sharing information, views, opinions, sentiments involving both these viruses. None prior this field analyzed focusing simultaneously. To address research gap, a total 61,862 simultaneously, posted between 7 May 2022 3 March 2023, were studied. findings contributions study are manifold. First, results using VADER (Valence Aware Dictionary sEntiment Reasoning) approach shows nearly half (46.88%) had negative sentiment. It was followed by positive (31.97%) neutral (21.14%), respectively. Second, paper presents top 50 hashtags used tweets. Third, it 100 most frequently words after performing tokenization, removal stopwords, word frequency analysis. indicate context included high level regarding COVID-19, viruses, President Biden, Ukraine. Finally, comprehensive comparative compares with 49 is presented further uphold relevance novelty work.

Language: Английский

Citations

27
Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing (NLP) Approach DOI Creative Commons
Ruth Olusegun, Timothy Oladunni,

Halima Audu

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 49882 - 49894

Published: Jan. 1, 2023

Emotion classification has become a valuable tool in analyzing text and emotions people express response to events or crises, particularly on social media other online platforms. The recent news about monkeypox highlighted various individuals felt during the outbreak. People's opinions concerns have been very different based their awareness understanding of disease. Although there studies monkeypox, emotion related this virus not considered. As result, study aims analyze individual expressed posts Our goal is provide real-time information identify critical To conduct our analysis, first, we extract preprocess 800,000 datasets then use NRCLexicon, Python library, predict measure emotional significance each text. Secondly, develop deep learning models Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bi-directional LSTM (BiLSTM), combination (CLSTM) for classification. We SMOTE (Synthetic Minority Oversampling Technique) Random Undersampling techniques address class imbalance training dataset. results revealed that CNN model achieved highest performance with an accuracy 96%. Overall, dataset can be powerful improving findings will help effective interventions improve public health.

Language: Английский

Citations

26
Hyper-parameter tuned deep learning approach for effective human monkeypox disease detection DOI Creative Commons

Neeraj Dahiya,

Yogesh Kumar Sharma, Uma Rani

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Sept. 23, 2023

Human monkeypox is a very unusual virus that can devastate society. Early identification and diagnosis are essential to treat manage an illness effectively. disease detection using deep learning models has attracted increasing attention recently. The causes may be passed people, making it zoonotic illness. latest epidemic hit more than 40 nations. Computer-assisted approaches Deep Learning techniques for automatically identifying skin lesions have shown viable alternative in light of the fast proliferation ever-growing problems supplying PCR (Polymerase Chain Reaction) Testing places with limited availability. In this research, we introduce model detecting human monkeypoxes accurate resilient by tuning its hyper-parameters. We employed mixture convolutional neural networks transfer strategies extract characteristics from medical photos properly identify them. also used hyperparameter optimization fine-tune Model get best possible results. This paper proposes Yolov5 model-based method differentiating between chickenpox Monkeypox on pictures. Roboflow lesion picture dataset was subjected three different strategies: SDG optimizer, Bayesian without Forgetting. proposed had highest classification accuracy (98.18%) when applied lesions. Our findings show suggested surpasses current best-in-class clinical settings actual diagnosis.

Language: Английский

Citations

21
Novel Sentiment Majority Voting Classifier and Transfer Learning-Based Feature Engineering for Sentiment Analysis of Deepfake Tweets DOI Creative Commons
Madiha Khalid, Ali Raza, Faizan Younas

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 67117 - 67129

Published: Jan. 1, 2024

Deepfake text known as synthetic text, involves using artificial intelligence (AI)-generated to create fabricated information or imitate actual individuals. Twitter tweets related deepfake can be used for many malicious intents, including impersonation, creating fake news, and spreading misinformation. The main goal of this investigation is detect people's sentiments technology with an advanced technique. A novel sentiment majority voting classifier (SMVC) proposed the labeling collected tweets. SMVC selects final from three lexicon-based models TextBlob, valence-aware dictionary reasoner (VADER), AFINN a mechanism. For classification, we propose transfer feature where embedding features are fed long short-term memory (LSTM), decision tree (DT) outputs combined into single set. Extensive experiments show that learning-based engineering results in highest performance. logistic regression outperforms accuracy 98.9% minimum computational complexity. classification performance each applied model validated k-fold cross-validations. Moreover, assessment existing state-of-the-art also carried out robustness

Language: Английский

Citations

6
Arabic sentiment analysis of Monkeypox using deep neural network and optimized hyperparameters of machine learning algorithms DOI
Hasan Gharaibeh, Rabia Emhamed Al Mamlook, Ghassan Samara

et al.

Social Network Analysis and Mining, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 24, 2024

Language: Английский

Citations

4
A turbulent flow optimized deep fused ensemble model (TFO-DFE) for sentiment analysis using social corpus data DOI

E. Aarthi,

S. Jagan,

C. Punitha Devi

et al.

Social Network Analysis and Mining, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 20, 2024

Language: Английский

Citations

4
Social media and the response to mpox DOI
David C. Coker, Tareq Mohammed Ali AL-Ahdal

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 101 - 113

Published: Jan. 1, 2025

Language: Английский

Citations

0
Optimizing chatbot responsiveness: Automated history context selector via three-way decision for multi-turn dialogue Large Language Models DOI

Weicheng Wang,

Xiaoliang Chen, Duoqian Miao

et al.

Engineering Analysis with Boundary Elements, Journal Year: 2025, Volume and Issue: 173, P. 106150 - 106150

Published: Feb. 12, 2025

Language: Английский

Citations

0
A BERT-based review helpfulness prediction model utilizing consistency of ratings and texts DOI
Xinzhe Li, Qinglong Li,

DongYeop Ryu

et al.

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(6)

Published: Feb. 15, 2025

Language: Английский

Citations

0