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Saurabh Goorha

dblp:04/1139 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author

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 · 50% Web and social media mining · 50%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Web and social media mining › social media analysis
social media monitoring
0.112010
Discovery of significant emerging trends · KDD 2010

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

signal-to-noise ratio analysis · 0.1phrase extraction · 0.1
YearPublicationVenuePosition
2010 Discovery of significant emerging trends
abstract
We describe a system that monitors social and mainstream media to determine shifts in what people are thinking about a product or company. We process over 100,000 news articles, blog posts, review sites, and tweets a day for mentions of items (e.g., products) of interest, extract phrases that are mentioned near them, and determine which of the phrases are of greatest possible interest to, for example, brand managers. Case studies show a good ability to rapidly pinpoint emerging subjects buried deep in large volumes of data and then highlight those that are rising or falling in significance as they relate to the firms interests. The tool and algorithm improves the signal-to-noise ratio and pinpoints precisely the opportunities and risks that matter most to communications professionals and their organizations.
Saurabh Goorha, Lyle H. Ungar
KDD1