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CATEGORIES:College of Arts and Sciences,College of Engineering,Graduate Stu
 dies,Lectures and Seminars,Thesis/Dissertations
DESCRIPTION:Advisor: Dr. José Domingo Mora - Associate Professor & Chairpe
 rson, Management & Marketing Committee Members:  Dr. Donghui Yan - Data S
 cience Co-Director & Department of MathematicsDr. Yuchou Chang - Departmen
 t of Computer & Information Science Date: Wednesday, August 19th, 2026Time
 : 11:00AM – 12:00 PM (Eastern Time)Location: Zoom (please contact: tpasu
 marthi@umassd.edu or josedomingo.mora@umassd.edu for Zoom information) Com
 mittee Members:  Dr. Donghui Yan - Data Science Co-Director & Department 
 of Mathematics     Dr. Yuchou Chang - Department of Computer & Informat
 ion Science Abstract: Online reviews are consumed in volume and evaluated 
 rapidly, making the linguistic properties of their earliest words conseque
 ntial. Mora and Izadi (2024) demonstrated that the grammatical and syntact
 ic composition of a review's opening carries diagnostic information about 
 the register of the full text, and that this register co-occurs with perce
 ived helpfulness. This thesis operationalizes and extends that account thr
 ough a reproducible seven-stage computational pipeline applied to 9,999 Am
 azon reviews drawn equally from the Books and Electronics domains. Review 
 openings were parsed for dependency and constituency structure, abstracted
  into canonical syntactic templates, embedded as sentence vectors, and clu
 stered using k-means. An eight-class taxonomy of opening strategies was se
 lected on the basis of clustering evaluation metrics and stability across 
 random initializations (mean adjusted Rand index = 0.99). The taxonomy was
  validated against manual annotation and tested for association with helpf
 ulness using nested negative binomial regression and for cross-domain gene
 ralizability using chi-square and Kruskal–Wallis tests. Opening class wa
 s significantly associated with helpfulness after controlling for review l
 ength, star rating, domain, and reviewer activity, and this association va
 ried by domain. Taxonomy composition was broadly stable across domains, co
 nfirming cross-domain generalizability. Validation further revealed that t
 he pipeline's embedding space captures semantic-functional organization ra
 ther than strictly syntactic structure, an empirical finding about how com
 putational methods represent register. The thesis contributes an automated
 , evaluated, and reproducible alternative to semi-manual register analysis
 . For further questions, please contact Professor José Domingo Mora at jo
 sedomingo.mora@umassd.edu.\nEvent page: https://www.umassd.edu/events/cms/
 20260819-syntactic-openings-in-online-reviews.php
X-ALT-DESC;FMTTYPE=text/html:<html><body><p>Advisor: <br />Dr. José Doming
 o Mora - Associate Professor & Chairperson\, Management & Marketing</p>\n<
 p>Committee Members:  <br />Dr. Donghui Yan - Data Science Co-Director & 
 Department of Mathematics<br />Dr. Yuchou Chang - Department of Computer &
  Information Science</p>\n<p>Date: Wednesday\, August 19th\, 2026<br />Tim
 e: 11:00AM – 12:00 PM (Eastern Time)<br />Location: Zoom (please contact
 : tpasumarthi@umassd.edu or josedomingo.mora@umassd.edu for Zoom informati
 on)</p>\n<p>Committee Members:  <br />Dr. Donghui Yan - Data Science Co-D
 irector & Department of Mathematics     <br />Dr. Yuchou Chang - Depart
 ment of Computer & Information Science</p>\n<p>Abstract:</p>\n<p>Online re
 views are consumed in volume and evaluated rapidly\, making the linguistic
  properties of their earliest words consequential. Mora and Izadi (2024) d
 emonstrated that the grammatical and syntactic composition of a review's o
 pening carries diagnostic information about the register of the full text\
 , and that this register co-occurs with perceived helpfulness. This thesis
  operationalizes and extends that account through a reproducible seven-sta
 ge computational pipeline applied to 9\,999 Amazon reviews drawn equally f
 rom the Books and Electronics domains. Review openings were parsed for dep
 endency and constituency structure\, abstracted into canonical syntactic t
 emplates\, embedded as sentence vectors\, and clustered using k-means. An 
 eight-class taxonomy of opening strategies was selected on the basis of cl
 ustering evaluation metrics and stability across random initializations (m
 ean adjusted Rand index = 0.99). The taxonomy was validated against manual
  annotation and tested for association with helpfulness using nested negat
 ive binomial regression and for cross-domain generalizability using chi-sq
 uare and Kruskal–Wallis tests. Opening class was significantly associate
 d with helpfulness after controlling for review length\, star rating\, dom
 ain\, and reviewer activity\, and this association varied by domain. Taxon
 omy composition was broadly stable across domains\, confirming cross-domai
 n generalizability. Validation further revealed that the pipeline's embedd
 ing space captures semantic-functional organization rather than strictly s
 yntactic structure\, an empirical finding about how computational methods 
 represent register. The thesis contributes an automated\, evaluated\, and 
 reproducible alternative to semi-manual register analysis.</p>\n<p>For fur
 ther questions\, please contact Professor José Domingo Mora at <a href="m
 ailto:josedomingo.mora@umassd.edu">josedomingo.mora@umassd.edu.</a></p><p>
 Event page: <a href="https://www.umassd.edu/events/cms/20260819-syntactic-
 openings-in-online-reviews.php">https://www.umassd.edu/events/cms/20260819
 -syntactic-openings-in-online-reviews.php</a></a></p></body></html>
DTSTAMP:20260806T164207
DTSTART;TZID=America/New_York:20260819T110000
DTEND;TZID=America/New_York:20260819T120000
LOCATION:Zoom - please contact: tpasumarthi@umassd.edu or josedomingo.mora@
 umassd.edu for Zoom information
SUMMARY;LANGUAGE=en-us:Syntactic Openings in Online Reviews: A Computationa
 l Pipeline for Cross-Domain Register Taxonomy and Helpfulness Analysis
UID:e2c79175626c2fa874631f75705a96ed@www.umassd.edu
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