Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/434743
Title: Unstructured data analytics of social network reviews using sentiment analysis
Researcher: Bairavel S
Guide(s): Krishnamurthy M
Keywords: Engineering and Technology
Computer Science
Computer Science Information Systems
Data Analytics
Social Network Reviews
Unstructured Data Analytics
Sentiment Analysis
User Generated Content
University: Anna University
Completed Date: 2021
Abstract: The rise of web 2.0 leads to a massive generation of User Generated Content (UGC) with different networks. UGC may be used and integrated to improve the performance of multimedia applications such as search engines, annotation, marketing, and recommendation by providing real-time semantic modeling. One of the prominent multimedia applications that have the researcher s interest and connect directly with the lives of individuals is the intelligent Travel Recommendation System (TRS). According to the World Travel and Tourism Council s statistics, the present generation heavily relies on information offered by various tourist websites before visiting a certain travel location throughout the world. Online travel services, like TripAdvisor, enable visitors to share views of the various destinations they have been in recent years. Due to the rapid development of these websites, a large amount of unstructured information prohibits users from quickly and effectively discovering desired tourist destinations. Different travel agencies are mostly focusing on personalized TRS to maximize their profits. The personalized TRS mainly exploits the user s preferences to provide the appropriate recommendations. There is a different sentiment associated with each review and it mainly specifies the user s preferences. In user attraction, the sentiment is either positive or negative. To design a context-aware TRS, sentimental analysis techniques play a major role. This thesis presents two sentimental analysis models that focus on both single and multiple modalities to offer improved performance with higher accuracy. newline newline
Pagination: xvii, 145p.
URI: http://hdl.handle.net/10603/434743
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File25.87 kBAdobe PDFView/Open
02_prelim pages.pdf492.39 kBAdobe PDFView/Open
03_content.pdf35.33 kBAdobe PDFView/Open
04_abstract.pdf132.56 kBAdobe PDFView/Open
05_chapter 1.pdf496.96 kBAdobe PDFView/Open
06_chapter 2.pdf385.02 kBAdobe PDFView/Open
07_chapter 3.pdf240.75 kBAdobe PDFView/Open
08_chapter 4.pdf857.41 kBAdobe PDFView/Open
09_chapter 5.pdf1.46 MBAdobe PDFView/Open
10_chapter 6.pdf255.54 kBAdobe PDFView/Open
11_annexures.pdf136.84 kBAdobe PDFView/Open
80_recommendation.pdf53.21 kBAdobe PDFView/Open
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