  Over the past decade humans have experienced exponential growth in the use of
online resources, in particular social media and microblogging websites such as
Facebook, Twitter, YouTube and also mobile applications such as WhatsApp, Line,
etc. Many companies have identified these resources as a rich mine of marketing
knowledge. This knowledge provides valuable feedback which allows them to
further develop the next generation of their product. In this paper, sentiment
analysis of a product is performed by extracting tweets about that product and
classifying the tweets showing it as positive and negative sentiment. The
authors propose a hybrid approach which combines unsupervised learning in the
form of K-means clustering to cluster the tweets and then performing supervised
learning methods such as Decision Trees and Support Vector Machines for
classification.
