  Social media has played a huge part on how people get informed and
communicate with one another. It has helped people express their needs due to
distress especially during disasters. Because posts made through it are
publicly accessible by default, Twitter is among the most helpful social media
sites in times of disaster. With this, the study aims to assess the needs
expressed during calamities by Filipinos on Twitter. Data were gathered and
classified as either disaster-related or unrelated with the use of Na\"ive
Bayes classifier. After this, the disaster-related tweets were clustered per
disaster type using Incremental Clustering Algorithm, and then sub-clustered
based on the location and time of the tweet using Density-based Spatiotemporal
Clustering Algorithm. Lastly, using Support Vector Machines, the tweets were
classified according to the expressed need, such as shelter, rescue, relief,
cash, prayer, and others. After conducting the study, results showed that the
Incremental Clustering Algorithm and Density-Based Spatiotemporal Clustering
Algorithm were able to cluster the tweets with f-measure scores of 47.20% and
82.28% respectively. Also, the Na\"ive Bayes and Support Vector Machines were
able to classify with an average f-measure score of 97% and an average accuracy
of 77.57% respectively.
