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 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.
