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dc.contributor.advisorMichael A. Cusumano.en_US
dc.contributor.authorLo, Bobbyen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2009-06-30T17:01:49Z
dc.date.available2009-06-30T17:01:49Z
dc.date.copyright2008en_US
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/46017
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.en_US
dc.descriptionIncludes bibliographical references (p. 89-93).en_US
dc.description.abstractSocial media is becoming increasingly important in society and culture, empowering consumers to group together on common interests and share opinions through the Internet. The social web shifts the originators of content from companies to users. Differences caused by this dynamic result in existing web analytic techniques being inadequate. Because people reveal their thoughts and preferences in social media, there are significant opportunities in business intelligence by analyzing social media. These opportunities include brand monitoring; trend recognition, and targeted advertising. The market for social media analytics in business intelligence is further validated by its direct application in the consumer research market. Challenges lie ahead for development and adoption of social media analytics. Technology used in these analytics, such as natural language processing and social network analysis, need to mature to improve accuracy, performance, and scalability. Nevertheless, social media continues to grow at a rapid pace, and organizations should form strategies to incorporate social media analytics into their business intelligence frameworks.en_US
dc.description.statementofresponsibilityby Bobby Lo.en_US
dc.format.extent93 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleSocial media analytics in business intelligence applicationsen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc367582343en_US


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