Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/305831
Title: An Integrated Sentiment Topic Mixture Model for Short Text in Identification of User Preference Emotion and Sarcasm
Researcher: Nimala.K
Guide(s): Jebakumar.R
Keywords: Computer Science
Computer Science Interdisciplinary Applications
Engineering and Technology
University: SRM University
Completed Date: 2020
Abstract: Social Media is a forum designed to allow people to create content, interact with society, share ideas, information, and lead a new way to socializing. Social media analytics is an art or practice of gathering data from blogs, social media websites, etc to get insights for arriving at a business decision. Social sentiment topics are sources that apply to all facets of the social analytics. Recently, researchers experienced the demand and challenges of analyzing short text. The first and the foremost challenge is the sparsity of the data and its reliance on the context. Secondly, there exists the presence of non-formal terms and noises such as slang words, misspellings, grammatical errors, abbreviations, or even foul languages. Thirdly, extracting sentiment feature, topic distribution feature and jointly capturing sentiment based topics is of great challenge. newlineThe Integrated Sentiment Topic Mixture (ISTM) framework was devised to address the three challenges in its design newline
Pagination: 
URI: http://hdl.handle.net/10603/305831
Appears in Departments:Department of Computer Science Engineering

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80_recommendation.pdfAttached File166.22 kBAdobe PDFView/Open
abstract.pdf26.01 kBAdobe PDFView/Open
acknowledgement.pdf25.76 kBAdobe PDFView/Open
chapter 1.pdf766.23 kBAdobe PDFView/Open
chapter 2.pdf670.39 kBAdobe PDFView/Open
chapter 3.pdf503.06 kBAdobe PDFView/Open
chapter 4.pdf727.12 kBAdobe PDFView/Open
chapter 5.pdf741.72 kBAdobe PDFView/Open
chapter 6.pdf744.05 kBAdobe PDFView/Open
chapter 7.pdf67.51 kBAdobe PDFView/Open
declaration.pdf34.85 kBAdobe PDFView/Open
list of publications.pdf67.33 kBAdobe PDFView/Open
preliminary pages.pdf171.95 kBAdobe PDFView/Open
references.pdf148.79 kBAdobe PDFView/Open
title.pdf108.62 kBAdobe PDFView/Open
vitae.pdf60.45 kBAdobe PDFView/Open


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