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Content Provider | ACM Digital Library |
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Author | Vaithyanathan, Shivakumar |
Abstract | Social media is an interactive vehicle for communication accessed on a daily basis by hundreds of millions of people. Unlike conventional media, which is a one-way street for information exchange, social media enables people to write content as well as provide feedback and recommend content to other users. There are multiple enterprise applications, such as customer retention, new customer acquisition, campaign management and lead generation that can significantly benefit from the consumer insights hidden in the massive amounts of social media content. Defining, extracting and representing entities such as people, organization and products, and their inter-relationships enables the building of comprehensive consumer profiles that can be leveraged in enterprise applications. Building these social media profiles requires a combination of text and entity analytics, while the utilization of such profiles makes heavy use of statistical models and machine learning. In this talk I will briefly describe the work in progress at IBM Research - Almaden on how such consumer insights, both at the level of an individual and at the level of appropriate micro-segments, can be used in enterprise applications in companies ranging from movie studios to financial services and insurance companies. I will also provide a brief overview of text, entity and statistical modeling tools that can operate in a distributed fashion over very large amounts of data. |
Starting Page | 755 |
Ending Page | 756 |
Page Count | 2 |
File Format | |
ISBN | 9781450312479 |
DOI | 10.1145/2213836.2213949 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2012-05-20 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Social media analytics Text analytics Hadoop Information extraction Large scale machine learning Entity resolution |
Content Type | Text |
Resource Type | Article |
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