Social Media Mining : Social Network

E-Interactivity and Social Media Introduction The introduction of the World Wide Web, enabled by the Internet, saw an explosive user growth. Consequently both online based and traditional companies with significant Internet presence started to realize the full potential of World Wide Web as a marketing medium. Since the popularization of the Internet and Internet related technologies, several new marketing paradigms have emerged that take advantage of Internet and Social Media. The use of social media

Data Mining: Detecting Insults in Social Media Conversations Capstone Progress Report 3 MIS 584 MIS Capstone University of Illinois at Springfield Spring 2016 Group 7 Team Members: Balogun, Oluwafunke Rathore, Rajsingh Shaikh, Firdous Farooque Srirangapalle, Dinesh Reddy Trivedi, Aditya   Table of Contents S. No. Headings Page No. 1 Introduction………………………………………………………………… 3 2 Problem Statement…………………………………………………………… 5 3 Significance of Project……………………………………………………… 6 4 Purpose……………….……………………………………………………

Social Media Mining: Sentiment Analysis Gloria Ngo Daniel Cano Hueros Andrew Gribben Cyril Jacob April 28, 2016  Table of Contents Table of Contents……………………………………………………………………………...1 Introduction…………………………………………………………………………………....2 Relating Social Media Mining to DSS Methodology…………………………………………2 Analysis………………………………………………………………………………………..3 Logical Design………………………………………………………………………………...4 Lessons Learned……………………………………………………………………………….8 References……………………………………………………………………………………10

What and how to analyze social data Sentiment Analysis (Opinion Mining). It is used to understand the emotion conveyed in a textual message. It involves identifying the opinion, extracting the features or objects for which the opinion is expressed and then categorizing the opinion as a positive, negative or neutral and thus assigning it a polarity (Liu 2010). The growth in social media provides a wider platform which has allowed for an abundance in the expression of opinions, including product reviews

Social Network Analysis: Approaches, Challenges and Mining Data Divya Sanjay Mittal (Under the guidance of Prof.Yong Ge) Department of Computer Science University of North Carolina at Charlotte dmittal1@uncc.edu Abstract — Social network data mining is an active research area in various disciplines, majorly sociology. Social media serves as a source of information for millions of people. People use social networks to receive information, share information about various subjects. Large amount of

redefined as a result of the array of information available due to social media and social networks. Organizations now face data overload, and are looking to increase both data breadth and data thickness. The most efficient and successful organizations are able to recognize this and adapt their strategy in order to incorporate social media and social data into its current market research techniques. There are six major benefits of using social data for marketing research; these include tracking trends for

that all moral agents should value privacy and individuality for themselves and others. When social media companies take sensitive and personal information to gain profit, it would be treating people as merely a means to an end. Without authority of consent, it would infringe on a person’s privacy and would not treat the person with the respect one deserves as a rational human being. To treat social network users with respect means they should be treated as people as ends in themselves and not as bits

INTRODUCTION OF DATA MINING USING IN CONTEMPORARY WORLD What is data mining Data mining generally is the process of analysing data from different perspectives and summarising it into useful information (Thuraisingham, 1999). It is also called the “Knowledge Discovery in Databases” process. It can be understand in the way of discovering interesting and useful patterns and relationships in large volumes of data. The overall goal of the data mining process is to extract information from a data set

as text, social media websites, images, audios, videos, e-commerce transactions, mobile devices, GPS signals, and sensors to collect climate data (Gunelius, 2013). Analytics is techniques that explore and extract intelligence from big data. Hence, big data analytics is a sub-process of the “insight extraction” process from big data

Privacy Issues in Data mining and Data Publishing: With the buzz around many big data applications, privacy concerns regarding their uses have also grown. With the personal data has been mined and published every day, the battle to reclaim the privacy starts vigorously. E-commerce websites harvests information about all the online searches of customers. Social Media exposes the likes and preferences of people, their photos and all their daily activities. Video surveillance monitors the movement of