The role of data mining and machine learning in sentiment analysis is
that they are essential in helping to advance not only this new field,
but they can also unearth new ways to navigate the nuances of human
narratives and teach computers to decipher human speech. For
example, Professor Yelena's hilarious yet clear analogy about whether
someone is sexy hot or temperature hot was an eye opening example
(for me personally). The role of technology can be boiled down to
enabling companies, analyze emotions and sentiments as part of their
mission; which is a valuable one (mission). Understanding employees
emotions helps to gain more insight that can lead to critical business
strategy as well as promoting good health and satisfaction. This
sounds simple but becomes challenging when you imagine a company
as big as Google or Twitter facing a task of processing unimaginable
volumes of unstructured data. Based on the varied texts we read this
week, sentiment mining techniques can be exploited for the creation
and automated upkeep, consumption, and review i of texts from many
different sources (social media posts, web content etc.). Gauging
people sentiments can help with engagement, product curation, and
lead to success; customer satisfaction is everything after all.
As an overcommitted individual, it can be difficult at times to motivate
myself into completing all the things on my to-do list each day.
However, this class has really helped to spike my interest and
stimulate my engagement because I see a gap in this field that needs to
be bridged, and with the knowledge we are all amassing in this course,
I feel that, by the end, we will be prepared and looking forward to
tackling our capstone project with fresh skills and brand new ideas.
