Machine Learning Models for Mining Social Media Data for Effective Natural Disaster Assessment DOI Creative Commons
Prahlada Varada Mittal,

Sejal Karki,

S. V. Parasher

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 18, 2023

Abstract Satellite technology has emerged as a key tool for effective management and assessment of natural disasters. However, the challenge accurately estimating impacted populations assessing building damage, often obscured from aerial views, persists. To address this, integration imagery textual data social networks offers promising solution. This study employs Twitter Flickr datasets, using SVM, CNN, XGBoost, Logistic Regression, Gradient Boost to extract insights. The sentiment analysis component categorizes disaster-affected individuals' emotions panic, neutral, or non-panic. Regression model excels in text classification, boasting an impressive 88.99% accuracy on test dataset 83.45% training. framework introduces Aid model, which gives us 83.16% classification tweets based aid sought by people through tweets. Image achieving 83.29% comprehend disaster impact visually. Given real-time media responses, system assists government organisations promptly, prioritising assistance. It serves dependable resource, enabling efficient responses tailored affected communities. Thus, this approach holds potential significantly enhance relief efficacy.

Language: Английский

Sentiment Analysis Based on Machine Learning Techniques: A Comprehensive Review DOI Open Access

Ari Ibrahim Hamid,

Adnan Mohsin Abdulazeez

Indonesian Journal of Computer Science, Journal Year: 2024, Volume and Issue: 13(3)

Published: June 15, 2024

In the landscape of digital communication, sentiment analysis stands out as a pivotal technology for deciphering vast troves unstructured text generated online. When integrated with machine learning, transforms into powerful tool capable distilling insights from complex human emotions and opinions expressed across social media, reviews, forums. This review paper embarks on thorough exploration integration learning techniques analysis, shedding light latest advancements, challenges, applications spanning various sectors including public health, finance, consumer behavior. It meticulously examines role in elevating through improved accuracy, adaptability, depth analysis. Furthermore, discusses implications these technologies understanding sentiment, tracking health trends, forecasting market movements. By synthesizing findings seminal studies cutting-edge research, this not only charts current but also forecasts trajectory underscores necessity ongoing innovation models to keep pace evolving discourse. The presented herein aim guide future research endeavors, highlight transformative impact outline potential new that could benefit society at large.

Language: Английский

Citations

0
Analysing Psychological Sentiment Prediction Across Modalities: Harnessing Emotion Datasets within Natural Language Processing (NLP) DOI
N Li,

Rong Kong

ACM Transactions on Asian and Low-Resource Language Information Processing, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Studying human emotions and feelings is a crucial element in the field of psychology, having significant implications such as evaluating mental health improving human-computer interactions. Recently, there has been rise interest examining how psychological sentiment can be predicted through various mediums, including text, audio, video, physiological signals. By utilizing advancements Natural Language Processing (NLP) analysing multimodal data, this research delves into incorporating emotion datasets NLP frameworks to improve prediction. This presents present an applicability some techniques predicts Psychological Sentiment Deep Generating adversarial networks (D-GANs), Long short-term memory (LSTM) gated recurrent unit (GRU). The sentimental analysis performed by considered algorithms are implemented python parameters which for results evaluation model loss, confusion matrix, accuracy, precision, recall f1-score. Our goal with offer deeper understanding existing methodologies future scope growth prediction NLP.

Language: Английский

Citations

0
Machine Learning Models for Mining Social Media Data for Effective Natural Disaster Assessment DOI Creative Commons
Prahlada Varada Mittal,

Sejal Karki,

S. V. Parasher

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 18, 2023

Abstract Satellite technology has emerged as a key tool for effective management and assessment of natural disasters. However, the challenge accurately estimating impacted populations assessing building damage, often obscured from aerial views, persists. To address this, integration imagery textual data social networks offers promising solution. This study employs Twitter Flickr datasets, using SVM, CNN, XGBoost, Logistic Regression, Gradient Boost to extract insights. The sentiment analysis component categorizes disaster-affected individuals' emotions panic, neutral, or non-panic. Regression model excels in text classification, boasting an impressive 88.99% accuracy on test dataset 83.45% training. framework introduces Aid model, which gives us 83.16% classification tweets based aid sought by people through tweets. Image achieving 83.29% comprehend disaster impact visually. Given real-time media responses, system assists government organisations promptly, prioritising assistance. It serves dependable resource, enabling efficient responses tailored affected communities. Thus, this approach holds potential significantly enhance relief efficacy.

Language: Английский

Citations

1