- published: 21 Jul 2016
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Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human...
Intro to video tutorial series for Mining Data from Social Media with Python ------ Channel link: https://goo.gl/nVWDos Subscribe here: https://goo.gl/gMdGUE Link to playlist: https://goo.gl/WIHiEy ---- Join my Facebook Group to stay connected: http://bit.ly/2lZ3FC5 Like my Facebbok Page for updates: https://www.facebook.com/tigerstylecodeacademy/ Follow me on Twitter: https://twitter.com/sukhsingh Profile on LinkedIn: https://www.linkedin.com/in/singhsukh/ ---- Schedule: New educational videos every week ----- Get the source code here: http://bit.ly/2nSQSAT
Based in Hyderabad (India) VPR Mining Infra Private Limited (VPRMIPL) is one of the leading CONTRACT MINING COMPANY with an annual turnover of around 100 Million USD. The company also has a global presence with operations in INDIA , Senegal and Indonesia which places it in a unique league among all LEADING CONTRACT MINING COMPANIES . Currently the company has major ongoing contracts with various prominent Central and State Govt. Public sector miners in India , MAJOR GROUPS IN SENEGAL & INDONESIA The company has a fleet of more than 500 small and large equipment at its disposal and commands a production capacity of more than 150 Million BCM per annum
Text analytics applied to social media has to date been fairly crude, mostly focusing on counting words of concepts mentioned in unstuctured text. The text analytics of Neurolingo took a more sophisticated lexicographic and grammatical analysis to messages from Twitter relating to sporting events and was able to detect messages that were focused in specific games and signalling people's intents - their wishes for the outcome, their feelings for the outcome and their predictions. The results were remarkably accurate forecasts, including tough calls for the 2012 Super Bowl and the 2012 NCAA college basketball finals that defied the predictions of odds-makers and many sports analysts. This same technology and methodology can be applied to determining the focus and intent of people in many sit...
This demo video will provide you with a quick overview of the Media Mining Client (MMC), SAIL LABS' powerful graphical user interface for turning raw, unstructured cross-media and cross-lingual data from open sources into actionable knowledge and intelligence. For more information, please contact us via: firstname.lastname@example.org www.sail-labs.com
This video is the first in a series that walks through all necessary steps for social media data mining and analysis with Raspberry Pi. Part 1 describes all the necessary hardware for the project and how to set up that hardware in just five minutes. Recorded for the University of Maine at Augusta.
Abstract—Students’ informal conversations on social media (e.g. Twitter, Facebook) shed light into their educational experiences—opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students’ experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative ana...
This video provides you with an overview of the SAIL LABS Media Mining System, an award-winning integrated platform for analysts and decision makers, extracting metadata and key information from multiple sources in multiple languages in real time. The Media Mining System consists of multiple components which are combined to provide a revolutionary capability: The power of cross-media and cross-lingual information retrieval, analytics and dissemination. For more information, please contact us via: email@example.com www.sail-labs.com
This video is seventh in a series for **absolute beginners** who would like to use an inexpensive, accessible computer called the Raspberry Pi in order to carry out social media data mining and analysis. In this installment, I walk through the process for storing social media data you've collected in the universally-accessible delimited format called CSV. We use the Python library CSV and consider ways to make a CSV format better organized and more useful. Coming up in installment number 8: working with Twitter and the csv.writer command to form data into appropriate shapes to characterize links, hashtags and relationships.
We've braved the dark of night, howling snowstorms and sub-zero conditions to bring you the story of Northshore Mining Railroad: The Little Giant! Formed out of the original Reserve Mining Railroad, this is the little railroad that could, did, and does in a big way! This program has it all. Explore the operations of the Northshore Mining Railroad with a look at the facilities in Silver Bay, rotary dumping activities, and giant lake freighters loading and unloading on Lake Superior! See the beautiful colors of fall in Northeastern Minnesota’s Arrowhead region. Listen to melodious airhorns echoing through the forest. Witness the brutal blast of winter as we film in all conditions, both day and night. Enjoy the 17,000 ton ore trains pulled by a pure EMD roster in crisp HD detail. This ...
After completing his PhD at Maastricht University in the Netherlands, Stephan worked as a consultant for a few years, but returned to academia because he missed teaching and “the liberty of choosing the projects I want to work on”. His current research projects include looking at what can be learned from how customers formulate their experiences and communicate with each other. “What can we learn about the customer experience from that? How do other consumers react? And, from a company’s perspective, how do messages need to be formulated to be more impactful and be heard by more people?”
Wits student, JohnnyJay Khalo is one of the very few who gets to enjoy the best of both worlds. Obtaining a BCS double degree in Mining engineering and Geology, he decided to join the media industry whilst juggling his studies. He talks about the transition, and what it felt like growing up queer in the Kasi. This video was shot and produced by Karen Mwendera.
Capturing Data, Modeling Patterns, Predicting Behavior. Capturing Data, Modeling Patterns, Predicting Behavior - Based on collecting more than 20 million blog posts and news media articles per day, Professor Jure Leskovec discusses how to mine such data to capture and model temporal patterns in the news over a daily time-scale --in particular, the succession of story lines that evolve and compete for attention. He discusses models to quantify the influence of individual media sites on the popularity of news stories and algorithms for inferring hidden networks of information flow. Learn more: http://scpd.stanford.edu/
Wer denkt, Vorratsdatenspeicherungen und „Big Data“ sind harmlos, der kriegt hier eine Demo an Spiegel-Online. Seit Mitte 2014 hat David fast 100.000 Artikel von Spiegel-Online systematisch gespeichert. Diese Datenmasse wird er in einem bunten Vortrag vorstellen und erforschen. David Kriesel
ENGS101P Individual video course work By Ana-Maria Belciug
"Everybody now is a storyteller...but when everybody's telling a story, which stories are worth listening to?" Google News Lab took a behind-the-scenes look at Storyful and its team of editors as they sift through eyewitness media posted to social platforms like YouTube and find the most newsworthy social content of the day, using a unique blend of technology and expert journalism.
This video is fifth in a series for absolute beginners who would like to learn how to mine and analyze social media data using an inexpensive, accessible computer called the Raspberry Pi. In this installment, I show Raspberry Pi owners how to install Tweepy, write a script in the programming language Python, and collect basic user and communication data from the social media platform Twitter. Coming up in installment number Six: how to STORE social media data you've collected into a permanent, well-organized database.
This video is ninth in a series for beginners in the use of an inexpensive, accessible Raspberry Pi computer to carry out social media data mining and analysis. In this installment, I explain and show how to modify your previous programs to allow for custom input -- this lets you run data-gathering programs very quickly and easily. To accomplish this, you only need to learn two simple commands related to user input and concatenation (the act of putting two pieces of text together).
Hier erkläre ich wie ich eure RockMiner R-Box mit einem Raspberry Pi betreiben könnt. Außerdem stelle ich meine Meiner Vor und halte Smalltalk über Bitcoin. Download link für das R-Box Raspberry Pi Image: http://goo.gl/yZzHYP RockMiner R-Box http://goo.gl/sulNLJ Antminer S1 http://goo.gl/hQ6jYR Lüfter http://goo.gl/DyHbbd Ich hoffe ich konnte euch weiter Helfen, Über ein Abbo würde ich mich sehr freuen :-)
In today’s world of data dominance, social networking websites and especially microblogging platforms, form the largest share in current unstructured textual data. If the proper tools, such as opinion mining and sentiment analysis are applied to that data, valuable information would be produced. That information in turn could offer insights from understanding market trends to interpreting social phenomena.The purpose of this thesis is the design and implementation of a system that deals with Network Analysis algorithms and visualisation of social networking data. Such a system consists of the following modules: Data retrieval is responsible for collecting data from social networking platforms. Data preprocessing methods cleans data of irrelevant information and prepares them for the applic...