The implementation of the Naive Bayes Algorithm was utilized to detect tweets related to disasters

Authors

  • Jayant Arsode Computer Science and Engineering Yeshwantrao Chavan College of Engineering Nagpur, India
  • Faizan Habib Computer Science and Engineering Yeshwantrao Chavan College of Engineering Nagpur, India

Keywords:

Twitter analysis, disaster identification, text analysis, document term matrix (DTM)

Abstract

Social media has become an important part of everyday life, with Twitter being a popular micro-blogging and social networking platform that allows users to share news, information, and personal thoughts. In times of emergencies or disasters, Twitter has proven to be a crucial communication medium. The widespread use of smartphones and tablets enables individuals to report emergencies in real-time, which can potentially save countless lives by alerting others to take necessary precautions. Several organizations are attempting to analyze tweets programmatically to detect disasters and emergencies, which can be beneficial to millions of internet users by providing timely alerts in the event of a crisis. However, the challenge lies in distinguishing between tweets related to a disaster and those that are unrelated. Twitter data is unstructured, making it necessary to use Natural Language Processing (NLP) to classify tweets as either "Related to Disaster" or "Not Related to Disaster". This research paper focuses on building a Naïve bayes classifier model and evaluating its accuracy by predicting on a test set created from the original dataset.

References

Goswami, Goswami,Goswami, Goswami, S. and and Raychaudhuri,Raychaudhuri, Raychaudhuri, Raychaudhuri, Raychaudhuri,Raychaudhuri,Raychaudhuri,Raychaudhuri,Raychaudhuri, D., D., 2020.2020.2020.2020.2020. IdentificationIdentificationIdentification Identification Identification Identification of Di sastersaster -Related Related Related TweetsTweets Tweets Using UsingUsing Natural NaturalNatural LanguageLanguage LanguageLanguageLanguageLanguageLanguage Processing: Processing: Processing:Processing:Processing: Processing:Processing: InternationalInternationalInternational InternationalInternational InternationalInternational ConferenceConferenceConferenceConferenceConference Conference Conference on RecentRecent Recent Recent TrendsTrendsTrendsTrendsTrendsTrends in Artificial Artificial Artificial Intelligence,Intelligence,Intelligence, Intelligence, Intelligence, Intelligence, IOT,IOT, IOT, Smart SmartSmartSmart Cities Cities Cities & Applications ApplicationsApplications ApplicationsApplications (ICAISC(ICAISC(ICAISC(ICAISC (ICAISC -2020).2020).2020).2020).2020).2020). IOT,IOT, SmartSmart SmartSmart CitiesCities & ApplicationsApplicationsApplicationsApplications Applications ApplicationsApplications (ICAISC(ICAISC(ICAISC(ICAISC(ICAISC(ICAISC(ICAISC-2020)(May2020)(May2020)(May2020)(May2020)(May2020)(May2020)(May 2020)(May 26,26,26, 2020)2020)2020)2020)2020)

Downloads

Published

29-04-2023

How to Cite

Jayant Arsode, & Faizan Habib. (2023). The implementation of the Naive Bayes Algorithm was utilized to detect tweets related to disasters. International Journal for Research Publication and Seminar, 14(3), 283–293. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/504

Issue

Vol. 14 No. 3 (2023): Vol 14 | Issue 3 | NCASIT 2023

Section

Original Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Re-users must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. This license allows for redistribution, commercial and non-commercial, as long as the original work is properly credited.