Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/489505
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dc.date.accessioned2023-06-07T05:39:15Z-
dc.date.available2023-06-07T05:39:15Z-
dc.identifier.urihttp://hdl.handle.net/10603/489505-
dc.description.abstractnewline Social Computing is the study of how people interact with computers and newlinetechnology in a social context. It involves aspects of Cognitive Science, Software newlineEngineering, Artificial Intelligence, Sociology, Anthropology, Psychology, and newlineOrganizational Behavior. The rise of Web 2.0 has led to an increase in user-generated newlinecontent. Examples of social computing include social networking sites, wikis, and newlineinstant messaging etc. Large-scale companies can benefit from social media in various newlineways, including improved web and mobile business performance. Social computing newlinecan also be used to study how people use technology to form and maintain newlinerelationships, share information, and collaborate. The field of social computing has newlineseen a growing interest in sentiment analysis in the recent years, due to its potential newlineapplications in various domains such as marketing, customer service, and opinion newlinemining. newlineSentiment analysis is a type of natural language processing that aims to determine newlinethe attitude or emotional state in a piece of text. The rapid expansion of Internet-based newlineapplications has led to comments, posts, and reviews regarding the daily activities of newlineInternet users. Sentiment analysis is often used to analyze social media posts, reviews, newlineand other forms of online communication. It can also be used to identify patterns and newlinetrends in how people feel about a particular topic, product, or brand. This information newlinecan be used by companies to improve their marketing strategies, or by researchers to newlinestudy the dynamics of online communities. newlineThis research work focuses primarily on the text data on Twitter, a social media newlineplatform. The objective of this research work is to detect and analyze the sentiment newlineexpressed by Twitter users. The research work explores the potential real-world newlineapplications of the proposed model and discusses its impact on the field of social newlinecomputing. newlineThe first goal of this research work is to propose a framework for sentiment newlineanalysis for Twitter text data. The proposed model in this frame
dc.format.extent
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleModels for Social Computing in Web Applications
dc.title.alternative
dc.creator.researcherSudheer Kumar Singh
dc.subject.keywordComputer Science
dc.subject.keywordCOMPUTER SCIENCE and ENGINEERING
dc.subject.keywordComputer Science Software Engineering
dc.subject.keywordEngineering and Technology
dc.description.note
dc.contributor.guidePrabhat Verma, Pankaj Kumar
dc.publisher.placeLucknow
dc.publisher.universityDr. A.P.J. Abdul Kalam Technical University
dc.publisher.institutionDean P.G.S.R
dc.date.registered2015
dc.date.completed2023
dc.date.awarded2023
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Dean P.G.S.R

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01_title.pdfAttached File299.99 kBAdobe PDFView/Open
02_prelim pages.pdf1.57 MBAdobe PDFView/Open
04_abstract.pdf482.8 kBAdobe PDFView/Open
05_chapter 1.pdf455.03 kBAdobe PDFView/Open
06_chapter 2.pdf347.88 kBAdobe PDFView/Open
07_chapter 3.pdf615.96 kBAdobe PDFView/Open
08_chapter 4.pdf750.65 kBAdobe PDFView/Open
09_chapter 5.pdf488.31 kBAdobe PDFView/Open
10_chapter 6.pdf892.43 kBAdobe PDFView/Open
11_chapter 7.pdf39 kBAdobe PDFView/Open
12_annexures.pdf261.56 kBAdobe PDFView/Open
80_recommendation.pdf188.53 kBAdobe PDFView/Open
content.pdf615.38 kBAdobe PDFView/Open


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