@book{6117,
	author = {Kavya Krishna K V (91616009)},
	title = {Know your doctor: Topic modeling and sentiment analysis based approach to review doctor},
	note = {Nowadays people tend to search for doctors through business review websites, they
naturally opt for those that  have the very best  ratings and an outsized variety of
reviews that support those high ratings. Hundreds or perhaps thousands of reviews
will  be given to the best-rated ones beneath their profiles,  and comparing a high
rated option to every alternative becomes a tedious task.  Furthermore, even if there
is only one highly-rated doctor, one may still want to read the reviews to see why
people like this doctor and if  the reviewers  addressed his or her concerns.  This,
again, could be time-consuming. In both cases,  some sort of review summarizer
would be helpful.
Web services such as Zocdoc and Yelp have offered their own version of “doctor
reviews” to help users quickly see what other reviewers have said about doctors.
Zocdoc rates doctors based on three categories: “overall rating,” “bedside manner,”
and “wait  time”.  However,  this does not cover any other useful points that  users
made in their specific reviews. Yelp automatically highlights representative review
sentences that share common phrases with other sentences, but no explicit rating is
given for the topics mentioned in those sentences.
This project  aimed at  building a tool would combine the best  of both the above
products. Know Your Doctor first detects the topics that have been discussed in the
reviews  (e.g.  bedside  manner).  Then,  it  analyzes  whether  people  were  talking
positively or negatively about those topics, and finally assigns appropriate ratings to
the  topics.  This  project  aims  to address  this  issue  by making a  summarizer  to
analyze  the  public  data  by  performing  topic  modeling  using  Latent  Dirichlet
Allocation(LDA), a standard Natural Language Processing (NLP) technique. LDA
is a tool which will determine topics from a corpus and word2vec based sentiment
analysis  which  is  the  computational  study  of  people's  opinions,  attitudes  and
emotions toward a review. Word2vec is a two-layer neural network that embeds the
text corpus to a set of feature vectors of the words in the corpus. The reviews are
taken from Yelp, an online rating website,  of doctors across San Francisco. As a
result  of  this  study,  a  snapshot  is  created  for  each  doctor  which  contain  most
dominant topics and their overall sentiment from their reviews.}
}