Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/14029
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTewari, Maitreyee
dc.date.accessioned2015-07-08T06:06:03Z
dc.date.available2015-07-08T06:06:03Z
dc.date.issued2015
dc.identifier.citationTewari, M.. (2015). Recognizing trust in natural language in Amazon's online reviews. 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. Indian Institute of Management, Ahmedabaden_US
dc.identifier.urihttp://hdl.handle.net/11718/14029
dc.description.abstractThe goal of this project is to build a system which could extract and classify ethotic statements (ethos relates to trustworthiness, credibility and reliability of seller) about Amazon’s service from a corpus of Amazon’s reviews. Until now processing and extracting ethos was done manually. With this project, we take the first step in automating the process of trust extraction from product reviews. The paper includes discussion on ethos extraction and has used natural language processing, machine learning and python to achieve the goals. Specifically we have used sentiment analysis, argument mining, python, supervised and semisupervised machine learning algorithms such as Naive Bayes and Maxent Classifiers. The contribution of this project is the development of two classifiers. One classifier that classifies sentences into ethos support and ethos attack and the other classifier that extracts ethotic statements from a corpus of Amazon reviews. These classifiers provide an initial solution to automatic ethos extraction.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management, Ahmedabaden_US
dc.relation.ispartofseriesIC 15;026
dc.subjectNatural Language Processingen
dc.subjectSentiment Analysisen
dc.subjectMachine Learningen
dc.subjectEthosen
dc.subjectTrust Extractionen
dc.titleRecognizing trust in natural language in Amazon's online reviewsen_US
dc.typeArticleen_US
Appears in Collections:4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

Files in This Item:
File Description SizeFormat 
IC 15-026.pdf
  Restricted Access
579.56 kBAdobe PDFView/Open Request a copy


Items in IIMA Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated.