Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/489505
Title: | Models for Social Computing in Web Applications |
Researcher: | Sudheer Kumar Singh |
Guide(s): | Prabhat Verma, Pankaj Kumar |
Keywords: | Computer Science COMPUTER SCIENCE and ENGINEERING Computer Science Software Engineering Engineering and Technology |
University: | Dr. A.P.J. Abdul Kalam Technical University |
Completed Date: | 2023 |
Abstract: | newline 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 |
Pagination: | |
URI: | http://hdl.handle.net/10603/489505 |
Appears in Departments: | Dean P.G.S.R |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 299.99 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 1.57 MB | Adobe PDF | View/Open | |
04_abstract.pdf | 482.8 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 455.03 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 347.88 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 615.96 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 750.65 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 488.31 kB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 892.43 kB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 39 kB | Adobe PDF | View/Open | |
12_annexures.pdf | 261.56 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 188.53 kB | Adobe PDF | View/Open | |
content.pdf | 615.38 kB | Adobe PDF | View/Open |
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