Natural hazards have the potential to cause catastrophic damage and significant
socioeconomic loss. The actual damage and loss observed in the recent decades has …

Social media analytics has become prominent in natural disaster management. In spite of a
large variety of metadata fields in social media data, four dimensions (ie space, time, content …

Sentiment analysis through machine learning using Twitter data has become a popular topic
in recent years. Here we address the problem of sentiment analysis during critical events …

Big data created by social media and mobile networks provide an exceptional opportunity to
mine valuable insights from them. This information is harnessed by business entities to …

Social media is an invaluable source of time-critical information during a crisis. However,
emergency response and humanitarian relief organizations that would like to use this …

Sentiment analysis refers to the class of computational and natural language processing
based techniques used to identify, extract or characterize subjective information, such as …

Coastal communities faced with multiple hazards have shown uneven responses and
behaviors. These responses and behaviors could be better understood by analyzing real …

Sentiment analysis has been widely researched in the domain of online review sites with the
aim of generating summarized opinions of users about different aspects of products …

People increasingly use microblogging platforms such as Twitter during natural disasters
and emergencies. Research studies have revealed the usefulness of the data available on …

We introduce a new approach to the maximum flow problem. This approach is based on
assigning arc lengths based on the residual flow value and the residual arc capacities. Our …