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 …

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

Sentiment analysis has become an important tool for e-commerce giant to capture user
sentiment towards their product and to exploit such analysis to attract user for buying …

Whenever a disaster occurs, users in social media, sensors, cameras, satellites, and the like
generate vast amounts of data. Emergency responders and victims use this data for …

A timely and reliable assessment of disaster impacts on highways is desired for executing
evacuations, providing emergency services, and planning relief and recovery activities in …

At present several vectorization approaches are used to transform text documents into a
numerical format. A huge number of features converted from text data from a single …

Proper formulation of features plays an important role in short-text classification tasks as the
amount of text available is very little. In literature, Term Frequency-Inverse Document …

In this paper, sentiment analysis of two critical events is presented using machine learning
(ML) techniques. COVID-19 has put immense pressure across the globe and sentiment …

Natural disasters have always been one of the threats to human societies. As a result of
such crises, many people will be affected, injured, and many financial losses will incur …