Know your doctor: Topic modeling and sentiment analysis based approach to review doctor (Record no. 6117)

MARC details
000 -LEADER
fixed length control field 03007nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122841.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180605b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Kavya Krishna K V (91616009)
9 (RLIN) 14316
245 ## - TITLE STATEMENT
Title Know your doctor: Topic modeling and sentiment analysis based approach to review doctor
300 ## - PHYSICAL DESCRIPTION
Extent MSC MI 2016-2018
500 ## - GENERAL NOTE
General note 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.
502 ## - DISSERTATION NOTE
Degree type MSC MI
Name of granting institution 2016-2018
Year degree granted INT
-- Mr. David Mathews
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element LATENT DIRICHLET ALLOCATION
9 (RLIN) 14317
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element NATURAL LANGUAGE PROCESSING
9 (RLIN) 14318
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element WORD2VEC
9 (RLIN) 14319
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   05/06/2018   R-1411 05/06/2018 05/06/2018 Project Reports