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Social media has played a huge part on how people get informed andcommunicate with one another. It has helped people express their needs due todistress especially during disasters. Because posts made through it arepublicly accessible by default, Twitter is among the most helpful social mediasites in times of disaster. With this, the study aims to assess the needsexpressed during calamities by Filipinos on Twitter. Data were gathered andclassified as either disaster-related or unrelated with the use of Na\"iveBayes classifier. After this, the disaster-related tweets were clustered perdisaster type using Incremental Clustering Algorithm, and then sub-clusteredbased on the location and time of the tweet using Density-based SpatiotemporalClustering Algorithm. Lastly, using Support Vector Machines, the tweets wereclassified according to the expressed need, such as shelter, rescue, relief,cash, prayer, and others. After conducting the study, results showed that theIncremental Clustering Algorithm and Density-Based Spatiotemporal ClusteringAlgorithm were able to cluster the tweets with f-measure scores of 47.20% and82.28% respectively. Also, the Na\"ive Bayes and Support Vector Machines wereable to classify with an average f-measure score of 97% and an average accuracyof 77.57% respectively.

Clustering Filipino Disaster-Related Tweets Using Incremental and  Density-Based Spatiotemporal Algorithm with Support Vector Machines for Needs  Assessment