Title: Sentiment analysis using various machine learning algorithms for disaster related tweets classification

Authors: S. Baby Sudha; S. Dhanalakshmi

Addresses: Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India ' Department of Software Systems, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India

Abstract: Once a crisis arises, people use social media platforms (such as Twitter) to communicate real-time updates. This data is incredibly helpful to disaster relief and response organisations and may offer rapid notifications for prioritising requests. Text mining and machine learning algorithms can scan enormous amounts of unstructured data created by social media outlets like Twitter to recognise disaster-related content based on keywords and phrases. One of the difficulties that algorithms may confront is determining whether the tweet content discusses actual disasters or uses these keywords as metaphors. As a result, this research aims to apply natural language processing (NLP) and classification models to discriminate between authentic and bogus disaster tweets. This dataset from the Kaggle website includes tweets about genuine disasters and fictional disasters. Four machine learning classifier methods were used: KNN, SVM, XGBoostand, and Naive Bayes. KNN offers the highest accuracy.

Keywords: disaster tweets; SVM; XGBoost; naïve Bayes; KNN; tweets classification; various machine learning algorithms; fakes and metaphors; disaster prediction task.

DOI: 10.1504/IJIEI.2023.136101

International Journal of Intelligent Engineering Informatics, 2023 Vol.11 No.4, pp.390 - 417

Received: 31 May 2023
Accepted: 13 Oct 2023
Published online: 16 Jan 2024 *

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