Classification of Disaster Tweets using Machine Learning and Deep Learning Techniques

Social networks provide a plethora of information for gathering extra data on people’s behavior, trends, opinions, and feelings during human-affecting occurrences, such as natural catastrophes. Twitter is an inevitable communication medium during calamities. People mainly depend on Twitter to announce real-time emergencies. However, it is rarely straightforward if someone is declaring a tragedy. Sentiment analysis of disaster tweets aid in situational awareness and realizing the disaster dynamics. In our paper, we perform a sentimental analysis of disaster tweets using techniques based on machine learning and deep learning. The tweets are pre-processed before being converted into a structured form using Natural Language Processing (NLP) methods. Supervised learning techniques such as the Support Vector Machine and the Naive Bayes Classifier algorithm are used to develop the Classifier, which categorizes tweets into distinct catastrophes and selects the most appropriate algorithm. The chosen algorithm is further enriched with an emoticon detection algorithm for explicit elucidation. Our research would help disaster relief organizations and news agencies to conclude about the state of affairs and do the needful.