Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/262031
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dc.coverage.spatialStatistics
dc.date.accessioned2019-11-01T05:15:05Z-
dc.date.available2019-11-01T05:15:05Z-
dc.identifier.urihttp://hdl.handle.net/10603/262031-
dc.description.abstractIntially, the concept of document models is discussed with respect to the Bernoulli approach, that is, basis is the presence or absence of primary blocks of the documents, namely tokens. The problem primarily deals with how an unstructured dataset consisting of text documents is converted to structured content with mathematical and statistical foundation. newlineThe focus in another problem is on Multinomial document model. It is similar to the Bernoulli model, but the presence flag in the former is now replaced with the frequentist method which takes into account the number of times the tokens occur in the text. newlineIn next problem, we move onto research of unsupervised topic modeling techniques to obtain latent topical structure across text documents and further fine-tuning with help of machine learning. Problem 4 deals with obtaining of key conversational drivers for textual data coupled with the attached sentiment and mood states. Such an approach helps in detecting the key drivers of conversation that is, whether it is having a positive impact or not or whether the text content has potential to become viral and much more. newlineIn last problem, we focus on Mood State and Behavior Prediction model in Social Media through Unstructured Data Analysis. Behavior Dirichlet Probability Model (BDPM), which can capture the Behavior and Mood of user on Social Media.
dc.format.extent158p.
dc.languageEnglish
dc.relation-
dc.rightsuniversity
dc.titleModelling techniques for unstructured data using machine learning and artificial intelligence approach
dc.title.alternative-
dc.creator.researcherBawa, Gurpreet Singh
dc.subject.keywordArtificial Intelligence
dc.subject.keywordMachine Learning
dc.subject.keywordNeural Networks
dc.subject.keywordPhysical Sciences,Mathematics,Statistics and Probability
dc.subject.keywordTextual Topic
dc.subject.keywordUnstructured Data
dc.description.noteReferences p.153-158
dc.contributor.guideSharma, Suresh K and Jain, Kanchan
dc.publisher.placeChandigarh
dc.publisher.universityPanjab University
dc.publisher.institutionDepartment of Statistics
dc.date.registered13/10/2014
dc.date.completed2019
dc.date.awardedn.d.
dc.format.dimensions-
dc.format.accompanyingmaterialCD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Statistics

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02_certificate.pdf451.14 kBAdobe PDFView/Open
03_acknowledgement.pdf245.04 kBAdobe PDFView/Open
04_contents.pdf396.42 kBAdobe PDFView/Open
05_chapter1.pdf1.32 MBAdobe PDFView/Open
06_chapter2.pdf1.17 MBAdobe PDFView/Open
07_chapter3.pdf1.13 MBAdobe PDFView/Open
08_chapter4.pdf2.44 MBAdobe PDFView/Open
09_chapter5.pdf2.07 MBAdobe PDFView/Open
10_chapter6.pdf716.37 kBAdobe PDFView/Open
11_chapter7.pdf380.15 kBAdobe PDFView/Open
12_references.pdf386.23 kBAdobe PDFView/Open


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