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TCSS-2021-12-0603.R1_Proof_hi.pdf (1.12 MB)
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Automated Disaster Monitoring from Social Media Posts using AI based Location Intelligence and Sentiment Analysis

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posted on 01.03.2022, 04:40 authored by Dr. Fahim SufiDr. Fahim Sufi, Ibrahim Khalil

Worldwide disasters like bushfires, earthquakes, floods, cyclones, heatwaves etc. have affected the lives of social media users in an unprecedented manner. They are constantly posting their level of negativity over the disaster situations at their location of interest. Understanding location-oriented sentiments about disaster situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI) and Natural Language Processing (NLP), for extraction of location-oriented public sentiments on global disaster situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to disaster in 110 languages through AI and NLP based sentiment analysis, named entity recognition (NER), anomaly detection, regression, and Getis Ord Gi* algorithms. We deployed and tested this algorithm on live

Twitter feeds from 28 September 2021 till 6 October 2021. Tweets with 67,515 entities in 39 different languages were processed during this period. Our novel algorithm extracted 9727 location entities with greater than 70% confidence from live twitter feed and displayed the locations of possible disasters with disaster intelligence. The rates of average precision, recall and F1-Score were measured to be 0.93, 0.88 and 0.90 respectively. Overall, the fully automated disaster monitoring solution demonstrated 97% accuracy. According to the best of our knowledge, this study is the first to report location intelligence with NER, sentiment analysis, regression and anomaly detection on social media messages related to disasters and has covered the largest set of languages.

History

Email Address of Submitting Author

contact@fahimsufi.com

ORCID of Submitting Author

0000-0002-9683-0839

Submitting Author's Institution

Federal Government

Submitting Author's Country

Australia