  Recent advances in Big Data has prompted health care practitioners to utilize
the data available on social media to discern sentiment and emotions
expression. Health Informatics and Clinical Analytics depend heavily on
information gathered from diverse sources. Traditionally, a healthcare
practitioner will ask a patient to fill out a questionnaire that will form the
basis of diagnosing the medical condition. However, medical practitioners have
access to many sources of data including the patients writings on various
media. Natural Language Processing (NLP) allows researchers to gather such data
and analyze it to glean the underlying meaning of such writings. The field of
sentiment analysis (applied to many other domains) depend heavily on techniques
utilized by NLP. This work will look into various prevalent theories underlying
the NLP field and how they can be leveraged to gather users sentiments on
social media. Such sentiments can be culled over a period of time thus
minimizing the errors introduced by data input and other stressors.
Furthermore, we look at some applications of sentiment analysis and application
of NLP to mental health. The reader will also learn about the NLTK toolkit that
implements various NLP theories and how they can make the data scavenging
process a lot easier.
