Kavya Krishna K V (91616009) <br/><br/>
Know your doctor: Topic modeling and sentiment analysis based approach to review doctor 

 - MSC MI 2016-2018 
<br/><br/> Nowadays people tend to search for doctors through business review websites, they<br/>naturally opt for those that  have the very best  ratings and an outsized variety of<br/>reviews that support those high ratings. Hundreds or perhaps thousands of reviews<br/>will  be given to the best-rated ones beneath their profiles,  and comparing a high<br/>rated option to every alternative becomes a tedious task.  Furthermore, even if there<br/>is only one highly-rated doctor, one may still want to read the reviews to see why<br/>people like this doctor and if  the reviewers  addressed his or her concerns.  This,<br/>again, could be time-consuming. In both cases,  some sort of review summarizer<br/>would be helpful.<br/>Web services such as Zocdoc and Yelp have offered their own version of “doctor<br/>reviews” to help users quickly see what other reviewers have said about doctors.<br/>Zocdoc rates doctors based on three categories: “overall rating,” “bedside manner,”<br/>and “wait  time”.  However,  this does not cover any other useful points that  users<br/>made in their specific reviews. Yelp automatically highlights representative review<br/>sentences that share common phrases with other sentences, but no explicit rating is<br/>given for the topics mentioned in those sentences.<br/>This project  aimed at  building a tool would combine the best  of both the above<br/>products. Know Your Doctor first detects the topics that have been discussed in the<br/>reviews  (e.g.  bedside  manner).  Then,  it  analyzes  whether  people  were  talking<br/>positively or negatively about those topics, and finally assigns appropriate ratings to<br/>the  topics.  This  project  aims  to address  this  issue  by making a  summarizer  to<br/>analyze  the  public  data  by  performing  topic  modeling  using  Latent  Dirichlet<br/>Allocation(LDA), a standard Natural Language Processing (NLP) technique. LDA<br/>is a tool which will determine topics from a corpus and word2vec based sentiment<br/>analysis  which  is  the  computational  study  of  people's  opinions,  attitudes  and<br/>emotions toward a review. Word2vec is a two-layer neural network that embeds the<br/>text corpus to a set of feature vectors of the words in the corpus. The reviews are<br/>taken from Yelp, an online rating website,  of doctors across San Francisco. As a<br/>result  of  this  study,  a  snapshot  is  created  for  each  doctor  which  contain  most<br/>dominant topics and their overall sentiment from their reviews. 
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<br/><br/><label>Subjects--Topical Terms: </label><br/>LATENT DIRICHLET ALLOCATION<br/>NATURAL LANGUAGE PROCESSING<br/>WORD2VEC