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Mining Application On Analyzing Users Interests From Twitter
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Book Synopsis Mining Application on Analyzing Users' Interests from Twitter by : Arti Jain
Download or read book Mining Application on Analyzing Users' Interests from Twitter written by Arti Jain and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today's world, it is problematic to provide users of social-media with posts that are analyzed from their interest efficiently. Users are unable to see the good quality and variety of posts based on their interest. The mass adoption of smartphones along with an internet connection via wi-fi or cellular network enables to analyse users' interest from Twitter. Twitter is used by a large number of audience to share their posts on a variety of topics as tweets. Then mining users' interests from Twitter can amplify a number of efficacies, such as advertising, trending topics that can be analyzed by interests and recommendation of users' posts. For this purpose, this paper provides an Android application which incorporates Web Services, Jsoup, JSON, Firebase Real-time Database and MVC. The application aids to select the posts which include spectacular images and text that are shown to users as a training set. The personalized posts can later be inferred and analyzed by the users themselves using Suffix Array Data Structure and Artificial Neural Network (ANN). Under ANN, we have used Backpropagation methodology that fires neurons as posts. Kosaraju algorithm and Palette library then help in removing redundant posts while later one also retaining relevant posts with specified hashtags more efficiently and accurately.
Book Synopsis Mining Twitter to Analyze the User Opinion on Products by : Deri Vaishnavi Dasari
Download or read book Mining Twitter to Analyze the User Opinion on Products written by Deri Vaishnavi Dasari and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increase of the internet today, it is very effective to share one's ideas with the world. With the fast growth of the Web, more and more people write reviews for all types of products and services and post them online. This data performs like guidance articulating the fulfillment of customers towards a specific item. It is becoming common practice for a customer to learn how others like or dislike a product before buying, or for a producer to have a track of customer views on its products to improve user satisfaction. But reading and searching through a massive amount of review text is time-consuming and monotonous work. Here comes the use of Sentimental Analysis also known as opinion mining. For analysis, we are going to use the machine learning technique sentiment analysis with NLP. Sentiment analysis is the process of defining the expressive tone behind a sequence of words, used to advance consideration of the attitudes, opinions, and feelings uttered within an online reference. Businesses do sentiment analysis to examine customers' opinions. We are going to collect the data from Twitter and analyze the data based on pattern Analyzer which sentiment analysis uses to obtain sentiment scores.
Book Synopsis Discovering Entities' Behavior Through Mining Twitter by : Hung Viet Tran
Download or read book Discovering Entities' Behavior Through Mining Twitter written by Hung Viet Tran and published by . This book was released on 2012 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: The unprecedented amount of user generated content from emerging social media platforms like Facebook and Twitter make them invaluable sources of information for research. Twitter in particular has about 500 million registered accounts globally who are generating approximately 340 million messages daily containing personal updates, general life observations, opinions, moods, etc. Twitter's vast amount of data, which is generally available, offers an ideal source for mining entities' behaviors. This thesis explores two research streams involving mining Twitter data. In the first work, we seek to understand the Twitter-based stakeholder communication strategies of firms. We analyze tweets posted by firms to build a system that can automatically predict target stakeholder groups of a given tweet. We also examine and incorporate firm characteristics into the system for performance improvement. The result will potentially provide valuable business intelligence to market analysts who would like to discover social media strategies and behaviors of firms. In the second work, we investigate how readers from different parts of the world react to news headlines through their Twitter messages. We design a framework for data collection, statistical analysis, sentiment analysis, and language model comparison to understand the interests and reactions of Twitter users towards news headlines. The results from this work can possibly help news organizations have better understanding of their audience for better services. Though the two research directions may seem distinct, there are points of connection. In both cases, we are interested in the impact of companies (firms and news organizations). Moreover the methods used are similar. Our results illustrate that just by gathering Twitter data stream and developing a framework to examine them, we are able to discover many interesting insights about news readers and firms.
Book Synopsis 21 Recipes for Mining Twitter by : Matthew Russell
Download or read book 21 Recipes for Mining Twitter written by Matthew Russell and published by "O'Reilly Media, Inc.". This book was released on 2011-02-08 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: Millions of public Twitter streams harbor a wealth of data, and once you mine them, you can gain some valuable insights. This short and concise book offers a collection of recipes to help you extract nuggets of Twitter information using easy-to-learn Python tools. Each recipe offers a discussion of how and why the solution works, so you can quickly adapt it to fit your particular needs. The recipes include techniques to: Use OAuth to access Twitter data Create and analyze graphs of retweet relationships Use the streaming API to harvest tweets in realtime Harvest and analyze friends and followers Discover friendship cliques Summarize webpages from short URLs This book is a perfect companion to O’Reilly's Mining the Social Web.
Book Synopsis Mining User Generated Content by : Marie-Francine Moens
Download or read book Mining User Generated Content written by Marie-Francine Moens and published by CRC Press. This book was released on 2014-01-28 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originating from Facebook, LinkedIn, Twitter, Instagram, YouTube, and many other networking sites, the social media shared by users and the associated metadata are collectively known as user generated content (UGC). To analyze UGC and glean insight about user behavior, robust techniques are needed to tackle the huge amount of real-time, multimedia,
Book Synopsis Twitter Data Analytics by : Shamanth Kumar
Download or read book Twitter Data Analytics written by Shamanth Kumar and published by Springer. This book was released on 2013-11-12 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief provides methods for harnessing Twitter data to discover solutions to complex inquiries. The brief introduces the process of collecting data through Twitter’s APIs and offers strategies for curating large datasets. The text gives examples of Twitter data with real-world examples, the present challenges and complexities of building visual analytic tools, and the best strategies to address these issues. Examples demonstrate how powerful measures can be computed using various Twitter data sources. Due to its openness in sharing data, Twitter is a prime example of social media in which researchers can verify their hypotheses, and practitioners can mine interesting patterns and build their own applications. This brief is designed to provide researchers, practitioners, project managers, as well as graduate students with an entry point to jump start their Twitter endeavors. It also serves as a convenient reference for readers seasoned in Twitter data analysis.
Book Synopsis Trends and Applications in Knowledge Discovery and Data Mining by : Xiao-Li Li
Download or read book Trends and Applications in Knowledge Discovery and Data Mining written by Xiao-Li Li and published by Springer. This book was released on 2015-11-25 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings at PAKDD Workshops 2015, held in conjunction with PAKDD, the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining in Ho Chi Minh City, Vietnam, in May 2015. The 23 revised papers presented were carefully reviewed and selected from 57 submissions. The workshops affiliated with PAKDD 2015 include: Pattern Mining and Application of Big Data (BigPMA), Quality Issues, Measures of Interestingness and Evaluation of data mining models (QIMIE), Data Analytics for Evidence-based Healthcare (DAEBH), Vietnamese Language and Speech Processing (VLSP).
Book Synopsis Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining by : Nitin Agarwal
Download or read book Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining written by Nitin Agarwal and published by Springer. This book was released on 2018-09-17 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributors in this book share, exchange, and develop new concepts, ideas, principles, and methodologies in order to advance and deepen our understanding of social networks in the new generation of Information and Communication Technologies (ICT) enabled by Web 2.0, also referred to as social media, to help policy-making. This interdisciplinary work provides a platform for researchers, practitioners, and graduate students from sociology, behavioral science, computer science, psychology, cultural studies, information systems, operations research and communication to share, exchange, learn, and develop new concepts, ideas, principles, and methodologies. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining will be of interest to researchers, practitioners, and graduate students from the various disciplines listed above. The text facilitates the dissemination of investigations of the dynamics and structure of web based social networks. The book can be used as a reference text for advanced courses on Social Network Analysis, Sociology, Communication, Organization Theory, Cyber-anthropology, Cyber-diplomacy, and Information Technology and Justice.
Book Synopsis Mining the Social Web by : Matthew Russell
Download or read book Mining the Social Web written by Matthew Russell and published by "O'Reilly Media, Inc.". This book was released on 2011-01-21 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they’re talking about, or where they’re located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed. Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools. Get a straightforward synopsis of the social web landscape Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn Learn how to employ easy-to-use Python tools to slice and dice the data you collect Explore social connections in microformats with the XHTML Friends Network Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits "Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python." --Jeff Hammerbacher, Chief Scientist, Cloudera "A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google
Book Synopsis Data Collection in a Social Network with Weighted Seed Selection and Data Analysis Based on Rule-based Methods by : Changhyun Byun
Download or read book Data Collection in a Social Network with Weighted Seed Selection and Data Analysis Based on Rule-based Methods written by Changhyun Byun and published by . This book was released on 2013 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, with the increasing popularity of diverse online social network sites, such as Facebook, Twitter, Blogger, YouTube, LinkedIn, and MySpace, a massive amount of data has become available. Analyzing sets of data in social media can lead to some understanding of individual and human behavior, detection of hot topics, identification of influential people, and/or discovery of a group or community. However, it is difficult to discover useful information from social data without automated information processing because of three main characteristics of social media data sets: the data is large, noisy, and dynamic. In order to overcome these challenges of social media, data-mining techniques can be used by data seekers to discover a diversity of perspectives that would otherwise not be possible. To apply data-mining techniques to social data, the target data set must be prepared from social networks before the analyzing process. For these reasons, Twitter enables researchers and data analyzers to access a variety of data in Twitter by providing Application Programming Interface (API). However, there is a restriction on data collection from Twitter: the method call of Twitter API is limited. Furthermore, it is impossible to collect enough data to apply data analysis techniques and filter out unnecessary data, such as spam messages without an automated data collector and filter. In order to overcome these data access problems, we aim to design and implement our own Twitter data-collection tool, which includes data filtering and analysis capabilities. This allows us, as well as other researchers and data seekers, to build their own Twitter dataset. First, in this research we introduce the design specifications and explain the implementation details of the Twitter Data Collecting Tool we developed. To introduce and explain the implementation details and the design specifications of the Twitter Data Collecting Tool, the Unified Modeling Language (UML) diagram is used. We next propose a new algorithm that selects the best seed nodes with limited resources and time to collect the data related to a specific topic and keyword efficiently. The algorithm also evaluates various user influence and activity factors, and updates the seed nodes dynamically during the gathering process. After the gathering process, we compared two results, one from this algorithm and one from a specialist. In the final chapter, we provide an analysis of Twitter data gathered by the Twitter Data Collecting Tool in a case study about the Super Bowl 2012 and Super Bowl 2013. The case study aims to address the question of how people use Twitter and to assess the power of Twitter in creating consumer interest in brands and commercials. The main objective of this study is to find the relationship between Twitter and Super Bowl advertisements by analyzing data on Twitter. This research shows that the Twitter Data Collecting Tool allows researchers to gather users' information, follow relationships and tweets from Twitter. Furthermore, the data collection result with the seed selection algorithm proved that the efficiency of the algorithm for collecting more keyword-related data is higher than the existing approach. In addition, data-mining techniques and rule-based data analysis are applied to the gathered data. With these results, we could prove that the Twitter Data Collecting Tool is able to gather a huge amount of data from Twitter and filter the data so it can be used in research areas. This paper will be valuable to those who may want to build their own Twitter dataset, apply customized filtering options to get rid of unnecessary, noisy data, and analyze social data to discover new knowledge.
Book Synopsis Cyber Crime and Forensic Computing by : Gulshan Shrivastava
Download or read book Cyber Crime and Forensic Computing written by Gulshan Shrivastava and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-09-07 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive study of different tools and techniques available to perform network forensics. Also, various aspects of network forensics are reviewed as well as related technologies and their limitations. This helps security practitioners and researchers in better understanding of the problem, current solution space, and future research scope to detect and investigate various network intrusions against such attacks efficiently. Forensic computing is rapidly gaining importance since the amount of crime involving digital systems is steadily increasing. Furthermore, the area is still underdeveloped and poses many technical and legal challenges. The rapid development of the Internet over the past decade appeared to have facilitated an increase in the incidents of online attacks. There are many reasons which are motivating the attackers to be fearless in carrying out the attacks. For example, the speed with which an attack can be carried out, the anonymity provided by the medium, nature of medium where digital information is stolen without actually removing it, increased availability of potential victims and the global impact of the attacks are some of the aspects. Forensic analysis is performed at two different levels: Computer Forensics and Network Forensics. Computer forensics deals with the collection and analysis of data from computer systems, networks, communication streams and storage media in a manner admissible in a court of law. Network forensics deals with the capture, recording or analysis of network events in order to discover evidential information about the source of security attacks in a court of law. Network forensics is not another term for network security. It is an extended phase of network security as the data for forensic analysis are collected from security products like firewalls and intrusion detection systems. The results of this data analysis are utilized for investigating the attacks. Network forensics generally refers to the collection and analysis of network data such as network traffic, firewall logs, IDS logs, etc. Technically, it is a member of the already-existing and expanding the field of digital forensics. Analogously, network forensics is defined as "The use of scientifically proved techniques to collect, fuses, identifies, examine, correlate, analyze, and document digital evidence from multiple, actively processing and transmitting digital sources for the purpose of uncovering facts related to the planned intent, or measured success of unauthorized activities meant to disrupt, corrupt, and or compromise system components as well as providing information to assist in response to or recovery from these activities." Network forensics plays a significant role in the security of today’s organizations. On the one hand, it helps to learn the details of external attacks ensuring similar future attacks are thwarted. Additionally, network forensics is essential for investigating insiders’ abuses that constitute the second costliest type of attack within organizations. Finally, law enforcement requires network forensics for crimes in which a computer or digital system is either being the target of a crime or being used as a tool in carrying a crime. Network security protects the system against attack while network forensics focuses on recording evidence of the attack. Network security products are generalized and look for possible harmful behaviors. This monitoring is a continuous process and is performed all through the day. However, network forensics involves post mortem investigation of the attack and is initiated after crime notification. There are many tools which assist in capturing data transferred over the networks so that an attack or the malicious intent of the intrusions may be investigated. Similarly, various network forensic frameworks are proposed in the literature.
Book Synopsis Advanced Data Mining and Applications by : Jianxin Li
Download or read book Advanced Data Mining and Applications written by Jianxin Li and published by Springer Nature. This book was released on 2019-11-16 with total page 894 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 15th International Conference on Advanced Data Mining and Applications, ADMA 2019, held in Dalian, China in November 2019. The 39 full papers presented together with 26 short papers and 2 demo papers were carefully reviewed and selected from 170 submissions. The papers were organized in topical sections named: Data Mining Foundations; Classification and Clustering Methods; Recommender Systems; Social Network and Social Media; Behavior Modeling and User Profiling; Text and Multimedia Mining; Spatial-Temporal Data; Medical and Healthcare Data/Decision Analytics; and Other Applications.
Book Synopsis Social Network Mining, Analysis, and Research Trends: Techniques and Applications by : Ting, I-Hsien
Download or read book Social Network Mining, Analysis, and Research Trends: Techniques and Applications written by Ting, I-Hsien and published by IGI Global. This book was released on 2011-12-31 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book covers current research trends in the area of social networks analysis and mining, sharing research from experts in the social network analysis and mining communities, as well as practitioners from social science, business, and computer science"--Provided by publisher.
Book Synopsis Mining, Modeling, and Analyzing Real-Time Social Trails by : Krishna Yeshwanth Kamath
Download or read book Mining, Modeling, and Analyzing Real-Time Social Trails written by Krishna Yeshwanth Kamath and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time social systems are the fastest growing phenomena on the web, enabling millions of users to generate, share, and consume content on a massive scale. These systems are manifestations of a larger trend toward the global sharing of the real-time interests, affiliations, and activities of everyday users and demand new computational approaches for monitoring, analyzing, and distilling information from the prospective web of real-time content. In this dissertation research, we focus on the real-time social trails that reflect the digital footprints of crowds of real-time web users in response to real-world events or online phenomena. These digital footprints correspond to the artifacts strewn across the real-time web like posting of messages to Twitter or Facebook; the creation, sharing, and viewing of videos on websites like YouTube; and so on. While access to social trails could benefit many domains there is a significant research gap toward discovering, modeling, and leveraging these social trails. Hence, this dissertation research makes three contributions: The first contribution of this dissertation research is a suite of efficient techniques for discovering non-trivial social trails from large-scale real-time social systems. We first develop a communication-based method using temporal graphs for discovering social trails on a stream of conversations from social messaging systems like instant messages, emails, Twitter directed or @ messages, SMS, etc. and then develop a content-based method using locality sensitive hashing for discovering content based social trails on a stream of text messages like Tweet stream, stream of Facebook messages, YouTube comments, etc. The second contribution of this dissertation research is a framework for modeling and predicting the spatio-temporal dynamics of social trails. In particular, we develop a probabilistic model that synthesizes two conflicting hypotheses about the nature of online information spread: (i) the spatial influence model, which asserts that social trails propagates to locations that are close by; and (ii) the community affinity influence model, which asserts that social trail prop- agates between locations that are culturally connected, even if they are distant. The third contribution of this dissertation research is a set of methods for social trail analytics and leveraging social trails for prognostic applications like real-time content recommendation, personalized advertising, and so on. We first analyze geo-spatial social trails of hashtags from Twitter, investigate their spatio-temporal dynamics and then use this analysis to develop a framework for recommending hashtags. Finally, we address the challenge of classifying social trails efficiently on real-time social systems. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/150975
Book Synopsis Social Media Mining and Social Network Analysis: Emerging Research by : Xu, Guandong
Download or read book Social Media Mining and Social Network Analysis: Emerging Research written by Xu, Guandong and published by IGI Global. This book was released on 2013-01-31 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social Media Mining and Social Network Analysis: Emerging Research highlights the advancements made in social network analysis and social web mining and its influence in the fields of computer science, information systems, sociology, organization science discipline and much more. This collection of perspectives on developmental practice is useful for industrial practitioners as well as researchers and scholars.
Author :Management Association, Information Resources Publisher :IGI Global ISBN 13 :1466624566 Total Pages :2335 pages Book Rating :4.4/5 (666 download)
Book Synopsis Data Mining: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources
Download or read book Data Mining: Concepts, Methodologies, Tools, and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2012-11-30 with total page 2335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining continues to be an emerging interdisciplinary field that offers the ability to extract information from an existing data set and translate that knowledge for end-users into an understandable way. Data Mining: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the latest advancements and developments of data mining and how it fits into the current technological world.
Book Synopsis Data Mining and Analysis in the Engineering Field by : Bhatnagar, Vishal
Download or read book Data Mining and Analysis in the Engineering Field written by Bhatnagar, Vishal and published by IGI Global. This book was released on 2014-05-31 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Particularly in the fields of software engineering, virtual reality, and computer science, data mining techniques play a critical role in the success of a variety of projects and endeavors. Understanding the available tools and emerging trends in this field is an important consideration for any organization. Data Mining and Analysis in the Engineering Field explores current research in data mining, including the important trends and patterns and their impact in fields such as software engineering. With a focus on modern techniques as well as past experiences, this vital reference work will be of greatest use to engineers, researchers, and practitioners in scientific-, engineering-, and business-related fields.
