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Matthew Denesuk

dblp:130/0309 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2Applied, interdisciplinary, general and emerging computing · 2Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 67% Web and social media mining · 33%
Software engineering, system software, and programming languages
1 paper
Services computing and microservices · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
customer relationship management
0.212013
A CRM system for social media: challenges and experiences · WWW 2013
Web and social media mining
social media analysis
0.212013
A CRM system for social media: challenges and experiences · WWW 2013
Data mining
text mining
0.212013
A CRM system for social media: challenges and experiences · WWW 2013
Services computing and microservices
enterprise systems
0.012013
A CRM system for social media: challenges and experiences · WWW 2013

Methods — techniques the papers use, named apart from their topics

conversation mining · 0.3
YearPublicationVenuePosition
2013 Content Analytics System for Social Customer Relationship Management
Meena Nagarajan, Danish Contractor, Stephen Dill, Jitendra Ajmera, Hyung-Il Ahn, Ashish Verma 0001, Matthew Denesuk
ICWSM7
2013 A CRM system for social media: challenges and experiences
abstract
The social Customer Relationship Management (CRM) landscape is attracting significant attention from customers and enterprises alike as a sustainable channel for tracking, managing and improving customer relations. Enterprises are taking a hard look at this open, unmediated platform because the community effect generated on this channel can have a telling effect on their brand image, potential market opportunity and customer loyalty. In this work we present our experiences in building a system that mines conversations on social platforms to identify and prioritize those posts and messages that are relevant to enterprises. The system presented in this work aims to empower an agent or a representative in an enterprise to monitor, track and respond to customer communication while also encouraging community participation.
Jitendra Ajmera, Hyung-Il Ahn, Meena Nagarajan, Ashish Verma 0001, Danish Contractor, Stephen Dill, Matthew Denesuk
WWW7