research publication. talks about nlp, ml, ai, etc for disaster management and assessment of natural disasters

1. Natural Disaster Twitter Data
Classification using CNN
and Logistic Regression
12th International Conference on SocProS 2023
Siddharth Parasher B. Tech 4th Year IK Gujral University, Punjab
Prahlada V. Mittal B. Tech 4th Year IIT Roorkee
Sejal Karki B. Tech 4th Year Graphic Era
Sukriti Narang B. Tech 4th Year Graphic Era
Ankush Mittal Director R&D (Sharda Univ) and Adjunct
Prof. CSE (IIT Mandi)

2. 45,000 annually

3. Objective
• Natural disasters pose significant
challenges in assessing population
impact and internal building
damage.
• Estimate affected population and
sentiment like panic and aid during
disasters.
• Categorizing damages into different
types for specialized aid provision

8. Crisis
MMD
CNN
Model
Output
Input
Panic Positive
Classification
Using Logistic
Regression
and SVM
Text Pre-
Processing
Using NLP
Data Augmentation
MEDIC
Selected Images
Text-based
Tweets
Non-Informative
Informative
Sentiment
Analysis
Panic Negative
Neutral
Output
Binary
Classification
Disaster Related
Disaster Unrelated
Output
Clustering
Using DBScan

9. CRISISMMD MEDIC
DATASET
14,223,141 tweets 71,000 images
tweet_text
text_info
text_human

10. Text Classification
TWEET
NON-INFORMATIVE
INFORMATIVE
Preprocessing tweets using NLP techniques to classify tweets
ML algorithms: XGBoost, SVM, Logistic Regression, and Gradient Boost.​
Best model: Logistic Regression (TF-IDF)
Test Accuracy- 88.99%

11. Result

12. Image Classification
• Deep Learning Approach using
CNN.
• We utilized a sequential
model from
TensorFlow's Keras API
• Accuracy: 83.29%.
• Confusion Matrix: Shows model
performance on predictions vs.
true labels.

13. Results
Class 1-Informative
Class 2-Non-Informative

14. Clusters(DB-
scan)
00
02
06
05
Caption
Caption
Other Relevant Info
Affected Individuals
Non-humanitarian
Vehicle Damage
01
03
04
Injured/Dead
Infrastructure Damage
Rescue Volunteer

15. Text clustering (DBScan)

16. Panic Assessment
Neutral
(Semi-Panic)
39.3%
Positive
(No Panic)
31.2%
Negative
(Panic)
31.2%
NLTK Library for sentiment intensity analysis.​

17. Impact
Assessing impact of disaster on people’s daily life
Enhancing disaster response and recovery efforts
Support for government agencies and organizations

18. CONCLUSION
DISASTER IMPACT
ASSESSMENT
UTILIZE SOCIAL MEDIA
DATA TO ESTIMATE THE
AFFECTED POPULATION
AND ASSESS BUILDING
DAMAGE DURING
DISASTERS.
SENTIMENT ANALYSIS
PERFORM SENTIMENT
ANALYSIS ON TWEETS TO
UNDERSTAND PUBLIC
REACTION, I.E., PANIC, NO
PANIC, OR NEUTRAL,
AIDING IN TARGETED
RELIEF EFFORTS.
MACHINE LEARNING
CLASSIFICATION
APPLY SVM, CNN,
XGBOOST, LOGISTIC
REGRESSION, AND
GRADIENT BOOST FOR
TEXT AND IMAGE
CLASSIFICATION TO
IDENTIFY RELEVANT
DISASTER-RELATED
CONTENT.
MODEL PERFORMANCE
EVALUATION
MEASURE THE ACCURACY
AND EFFECTIVENESS OF
VARIOUS MACHINE
LEARNING MODELS ON
BOTH TEST AND TRAIN
DATA.
RAPID AID PROVISION
SHOWCASE HOW
IMMEDIATE SOCIAL MEDIA
REACTIONS CAN ASSIST
GOVERNMENT AGENCIES
AND ORGANIZATIONS IN
PROVIDING AID PROMPTLY
TO AFFECTED REGIONS
BASED ON PRIORITY.

19. Q&A
Feel free to ask any
questions