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Visual Text Mining in Social Media
 
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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 application of the sentiment analysis method. Sentiment Analysis applies a model to the data in order to classify them according to their sentiment. Data Reprocessing prepares the data for the visualization process. Topic Modeling applies specific algorithms that identify topics in text corpora. Visualization process represents data in a graph, taking into account the results of all previous processes.
Views: 2544 Manolis Maragoudakis
Shedding Light on Social Media with Text Analytics
 
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Unleash the insights from social media data with text analysis. SAS shares examples of value delivered to organizations. To learn more go to: http://www.sas.com/voice
Views: 2959 SAS Software
Text Mining for Social Scientists
 
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Text mining refers to digital social research methods that involve the collection and analysis of unstructured textual data, generally from internet-based sources such as social media and digital archives. In this webinar, Gabe Ignatow and Rada Mihalcea discussed the fundamentals of text mining for social scientists, covering topics including research design, research ethics, Natural Language Processing, the intersection of text mining and text analysis, and tips on teaching text mining to social science students.
Views: 888 SAGE
Social Media Analytics - Twitter Analysis in R (Example @realDonaldTrump)
 
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Case Study: Donald Trump Twitter (@realDonaldTrump) Analysis Click here to see how to link to Twitter database: https://www.youtube.com/watch?v=ebutXE4MJ3Y (UPDATED) Twitter Analytics in R codes Powerpoint can be downloaded at https://drive.google.com/open?id=0Bz9Gf6y-6XtTNDE5a2V0dXBjWVU How to process tweets with emojis in R? What if there is a gsub utf-8 invalid error? (Example Solution) 1. Use gsub to replace the emojis (utf-8 coding) codes. 2. See slide 7 in the Powerpoint file above.
Views: 4603 The Data Science Show
Text Mining Social Media Sentiment Analytics in  R-11th June 2016
 
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Analytics Accelerator Program- May 2016-July 2016 Batch
SAGE Campus: Introduction to Text Mining – Social media
 
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Gabe Ingnatow explores the advantages and limitations of using social media to acquire data. Find out more about Introduction to Text Mining and all our online courses at: campus.sagepub.com
Views: 7 SAGE
Sentiment Analysis of Social Media Texts Part 1
 
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Sentiment Analysis of Social Media Texts Saif M. Mohammad and Xiaodan Zhu October 25, 2014 - Morning Tutorial notes Abstract: Automatically detecting sentiment of product reviews, blogs, tweets, and SMS messages has attracted extensive interest from both the academia and industry. It has a number of applications, including: tracking sentiment towards products, movies, politicians, etc.; improving customer relation models; detecting happiness and well-being; and improving automatic dialogue systems. In this tutorial, we will describe how you can create a state-of-the-art sentiment analysis system, with a focus on social media posts. We begin with an introduction to sentiment analysis and its various forms: term level, message level, document level, and aspect level. We will describe how sentiment analysis systems are evaluated, especially through recent SemEval shared tasks: Sentiment Analysis of Twitter (SemEval-2013 Task 2, SemEval 2014-Task 9) and Aspect Based Sentiment Analysis (SemEval-2014 Task 4). We will give an overview of the best sentiment analysis systems at this point of time, including those that are conventional statistical systems as well as those using deep learning approaches. We will describe in detail the NRC-Canada systems, which were the overall best performing systems in all three SemEval competitions listed above. These are simple lexical- and sentiment-lexicon features based systems, which are relatively easy to re-implement. We will discuss features that had the most impact (those derived from sentiment lexicons and negation handling). We will present how large tweet-specific sentiment lexicons can be automatically generated and evaluated. We will also show how negation impacts sentiment differently depending on whether the scope of the negation is positive or negative. Finally, we will flesh out limitations of current approaches and promising future directions. Instructors: Saif M. Mohammad, Researcher, National Research Council Canada Saif Mohammad is a Research Officer at the National Research Council Canada. His research interests are in Computational Linguistics, especially Lexical Semantics. He develops computational models for sentiment analysis, emotion detection, semantic distance, and lexical-semantic relations such as word-pair antonymy. Xiaodan Zhu, Researcher, National Research Council Canada Xiaodan Zhu is a Research Officer at the National Research Council Canada. His research interests are in Natural Language Processing, Spoken Language Understanding, and Machine Learning. His recent work focuses on sentiment analysis, emotion detection, speech summarization, and deep learning. The instructors, along with Svetlana Kiritchenko, developed the NRC-Canada Sentiment Analysis System, which was the top-performing system in recent SemEval shared-task competitions (SemEval-2013, Task 2, SemEval-2014 Task 9, and SemEval-2014 Task 4).
Views: 31876 emnlp acl
Facebook text analysis on R
 
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For more information, please visit http://web.ics.purdue.edu/~jinsuh/.
Views: 11346 Jinsuh Lee
Social Media Marketing and Management - Data Mining - Text Mining - Sentimental Analysis
 
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-Explanation: A Social Media Marketing and Management Project -Lesson: Data Mining -Subject: Sentimental Analysis ( Emotional Analysis ) of Text Mining ----------- -Açıklama: Sosyal Medya ve Pazarlama Uygulaması Projesi -Ders: Veri Madenciliği -Konu: Duygusal Metin Analizi
Views: 164 Egemen Kayalidere
R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 21264 DataCamp
Interview with Social Media/Text Analytics Guru Seth Grimes
 
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Wide ranging interview with Seth Grimes of Altaplana and the Sentiment Symposium events on the state of social media and text analytics, what the future holds for that space, and how brands are using those technologies to drive business impact.
Views: 708 Leonard Murphy
Text Mining in JMP with R
 
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Some estimates suggest that unstructured text accounts for roughly 80 percent of the information stored by most organizations. This presentation by Andrew T. Karl, Senior Management Consultant at Adsurgo LLC, and Heath Rushing, Principal Consultant and Co-Founder of Adsurgo LLC, provides an overview of methods easily implemented with the R interface to JMP to find previously unknown relationships from a collection of unstructured data. By utilizing R packages for text mining and sparse matrix algebra, JMP may be equipped to extract information from text without requiring end-user knowledge of R. The text -- which may be from emails, survey comments, social media, incident reports, insurance claim reports, etc. -- may be used for several purposes. Vectors from a singular value decomposition of the document term matrix produced in R may be added to the original data table in JMP and included in predictive models (e.g., via the Fit Model or Neural platforms) or clustering algorithms (via the Cluster platform). Another goal may be to explore the underlying themes of the text though word counts or latent semantic indexing. We will demonstrate a JSL/R script that provides such functionality. This presentation was recorded at Discovery Summit 2013 in San Antonio, Texas.
Views: 5483 JMPSoftwareFromSAS
Intro to Text Mining Sentiment Analysis using R-12th March 2016
 
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Analytics Accelerator Program, February 2016-April 2016 batch
Views: 23040 Equiskill Insights LLP
Dr Stephan Ludwig - How text mining in social media makes for smarter data?
 
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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?”
Social Media Text Analytics - Mining Value From Predictive Insights
 
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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 situations, including key enterprise settings. NOTE: some problems with flicker in this video related to the Google Hangouts technology.
Views: John Blossom
Quantitative Text Analysis for Social Scientists  A talk by Nicole Rae Baerg
 
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Nicole Rae Baerg, lecturer at the Department of Government at the University of Essex, discusses qualitative text analysis in this SAGETalks webinar. Text analysis has a long history in the social sciences and has been commonly used to analyze media coverage. Historically, it involved the human coding of text and this has inherent issues. The digital age has made huge amounts of data available for analysis in the form of newspapers, blogs, social media feeds, government documents, the list goes on! As the technology to automate the analysis and coding of texts has become more available we are able to go beyond this and treat text as quantifiable data. Watch this webinar to learn about the role that quantitative text analysis plays for social scientists when working with such vast amounts of data, as well hearing about ‘QTA in action’ and how social scientists are using text analysis for their research.
Views: 437 SAGE
Tourist behavior analysis using social media and text analytics
 
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Tool created in order to analyze tourist behavior using social media. With this tool it is possible to correctly identify where tourists came from and where they go, also if they liked the foreign country using text analytics. It is also possible to get tourist spending patterns (how much they spend).
Views: 1014 João Ladeira
R - Twitter Mining with R (part 1)
 
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Twitter Mining with R part 1 takes you through setting up a connection with Twitter. This requires a couple packages you will need to install, and creating a Twitter application, which needs to be authorized in R before you can access tweets. We quickly go through this entire process which may take some flexibility on your part so be patient and be ready troubleshoot as details change with updates. Warning: You are going to face challenges setting up the twitter API connection. The steps for this part have been known to change slightly over time for a variety of reasons. Follow the general steps and expect a few errors along the way which you will have to troubleshoot. It is hard to solve these issues remotely from where I am.
Views: 62938 Jalayer Academy
Analysing Demonetisation through Text Mining using Live Twitter Data!
 
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Learn how Text Mining can be used to Analyse Social media data for insights into real world problems like Demonetisation! Our Expert Uses Live Twitter data to teach Text mining on Demonetisation.
Views: 1255 IvyProSchool
Social Media Mining
 
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Hundreds of millions of people spending countless hours on social media to share, communicate, connect, interact, and create user-generated data. Using data mining, machine learning, text mining, social network analysis, and information retrieval, we could mine valuable knowledge for social science researches and business marketing proposes. This project was our graduation project. we used a real data from Facebook to give a proper recommendation for users about movies and series due to the social group that our users belongs to, we also managed to recommend friends to a user due to interests similarity.
" Pseudo Feature Extraction in Social Network Analysis and Text Mining" by Mr. Ibrahim Almosallam
 
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The First International Webinar " We are all social things " - IS Dept - CCIS - KSU For more details , please visit the webinar site : https://sites.google.com/site/iswebnira2013/home
Views: 220 [email protected]
Text Mining, Web Scraping and Sentiment Analysis with R - Social Media Analysis by R-Tutorials.com
 
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R-Tutorials offers a variety of R courses ranging from beginner to advanced levels. Full courses are hosted on Udemy. Find the links to our webpage (http://www.r-tutorials.com) as well as to our courses (free and paid) below: R Basics beginners FREE course https://www.udemy.com/r-basics/ R Level 1 intermediate course - 60% OFF https://www.udemy.com/r-level1/?couponCode=youtube19 Statistics in R advanced course - 50% OFF https://www.udemy.com/statisticsinr/?couponCode=youtube29 Machine Learning and Statistical Modeling with R Examples - 75% OFF https://www.udemy.com/machine-learning-and-statistical-modeling-with-r/?couponCode=youtube17 Graphs in R advanced course - 70% OFF https://www.udemy.com/graphs-in-r/?couponCode=youtube29 Social Media Analysis advanced course - 56% OFF https://www.udemy.com/r-social-media-mining-scraping-with-twitter/?couponCode=youtube29 Excel 2013 Charts beginners FREE course https://www.udemy.com/excel-charts/ Audacity for Instructors and Podcasters 50% OFF https://www.udemy.com/audacity-mastery-course-for-instructors-and-podcasters/?couponCode=youtube15
Views: 3105 R Tutorials
Mining Online Data Across Social Networks
 
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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/
Views: 19843 stanfordonline
Quantitative Text Mining, the Social Scientific Way: Mining Social Media on Brexit
 
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Presented by Prof. Kenneth Benoit, Professor of Quantitative Social Research Methods at the London School of Economics, at the Cambridge Artificial Intelligence Summit, hosted by Cambridge Spark. cambridgespark.com
Views: 120 Cambridge Spark
Text Analytics and Social Media Monitoring from Veda Semantics
 
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An introduction to Veda Semantics, focusing on Natural Language Processing, Sentiment Analysis, and Text Mining. Introduces the Veda products of Veda Discovery, Veda Prism and Veda Txt. Uses in social media monitoring, social listening, sentiment analysis, text mining in Big Data scenarios, and VoC analysis.
Views: 404 Veda Semantics
About Colourtext - Natural Language Processing through Sentiment Analysis & Text Mining
 
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An Introduction to Colourtext: A semantic analysis dashboard based on a world-leading Natural Language Processing Engine, and a powerful data discovery platform. Colourtext uses emotional intelligence to process natural language text. We can process natural language text from: CRM Social Media Market Research
Views: 358 Colourtext
Industry Applications of Text Analytics
 
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An overview of why text analytics is useful, and how it can help areas such as surveys, chat bots, social media monitoring, voice of customer, call-logs, and more.
Views: 42986 Lexalytics
How to analyse Social Media data from Twitter in Tableau
 
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Learn how to analyse Social Media data from Twitter in Tableau Complete playlist: https://www.youtube.com/playlist?list=PLm6YM4S6arkfVaiTCF16VSZSmGiJ7kHic Further reading: http://alexloth.com/2017/09/12/social-media-customer-centric-data-strategy-data17-resources/
Views: 2193 Alexander Loth
Text Mining Solutions Promo
 
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The video combines text and graphics with music to create a short promotional video highlighting the benefits of Text Mining Solutions that can be be shared via email and social media. It is also hosted on the client's website, it complements the text and provides an engaging summary of TMS's services. Video also helps boost Google ranking.
Social Media Mining and Analytics Presentation
 
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Recorded with http://screencast-o-matic.com
Views: 183 Jennifer Paiotti
Talk Data to Me: Let's Analyze Social Media Data with Tableau
 
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Social media data is hot stuff—but it sure can be tricky to understand. In this session, Michelle from Tableau's social media team will share how they analyze social media data from multiple sources. We'll compare methods for collecting data, and discuss tips for ensuring that it answers new questions as they arise. Whether you're new to social media analysis or have already started diving into your data, this session will provide key tips, tricks, and examples to help you achieve your goals.
Views: 11028 Tableau Software
Introduction to Text Analytics with R: Overview
 
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This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data is far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: - Tokenization, stemming, and n-grams - The bag-of-words and vector space models - Feature engineering for textual data (e.g. cosine similarity between documents) - Feature extraction using singular value decomposition (SVD) - Training classification models using textual data - Evaluating accuracy of the trained classification models Part 1 of this video series provides an introduction to the video series and includes specific coverage: - Overview of the spam dataset used throughout the series - Loading the data and initial data cleaning - Some initial data analysis, feature engineering, and data visualization Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam... The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/tu... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3500+ employees from over 700 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 56166 Data Science Dojo
Social Network Analysis with R | Examples
 
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Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 14083 Bharatendra Rai
Eulalia Veny - Recipe for text analysis in social media
 
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Recipe for text analysis in social media: [EuroPython 2018 - Talk - 2018-07-25 - PyCharm [PyData]] [Edinburgh, UK] By Eulalia Veny The analysis of text data in social media is gaining more and more importance every day. The need for companies to know what people think and want is key to invest money in providing customers what they want. The first approach to text analysis was mainly statistical, but adding linguistic information has been proven to work well for improving the results. One of the problems that you need to address when analyzing social media is time. People are constantly exchanging information, users write comments every day about what they think of a product, what they do or the places they visit. It is difficult to keep track of everything that happens. Moreover, information is sometimes expressed in short sentences, keywords, or isolated ideas, such as in Tweets. Language is usually unstructured because it is composed of isolated ideas, or without context. I will talk about the problem of text analysis in social media. I will also explain briefly Naïve Bayes classifiers, and how you can easily take advantage of them to analyse sentiment in social media, and I will use an example to show how linguistic information can help improve the results. I will also evaluate the pros and cons of supervised vs unsupervised learning. Finally, I will introduce opinion lexicons, both dictionary based and corpus-based, and how lexicons can be used in semi-supervised learning and supervised learning. If I have time left, I will explain about other use cases of text analysis. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2018.europython.eu/en/speaker-release-agreement/
Social Media Data Mining With Raspberry Pi (Part 3: Operating Systems)
 
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This video is third in a series that walks through all the steps necessary to mine and analyze social media data using the inexpensive computer called a Raspberry Pi. Part 3 describes the two operating system environments of the Raspberry Pi: the Windows-like graphic user interface and the Linux text-based terminal environment.
Views: 1248 James Cook
Data mining in social media
 
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I used screencast-o-matic to record my presentation.
Views: 346 Bryan Russowsky
Social Media Mining
 
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Social Media Mining
Views: 364 WMAR-2 News
Sentiment Analysis of 700,000 tweets from Super Bowl 50 - RapidMiner & AYLIEN
 
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We collected over 700,000 tweets during the Super Bowl to decide who won the ads battle We've teamed up with the guys at RapidMiner, the leaders in Predicitve Analytics to showcase the power of mining unstructured data, text in particular, as part of your data analytics strategy. Mining Customer Opinion on Social Channels During this webinar, RapidMiner and AYLIEN will explore the power of social content from a data analytics point of view by analyzing tweets collected during Super Bowl 50. This webinar will show how easy it is to collect, analyze and visualize social content using RapidMiner and the AYLIEN extension. We'll be analyzing the public reaction to the ads that ran as part of Super Bowl 50. We'll show you how to: - Leverage predictive and text analytics for: understanding your clients, improving customer satisfaction, and optimizing marketing spend - Make sense of social media data across thousands of responses, using sentiment analysis and predictive modeling - Understand the impact of predictive and text analytics on business opportunities - Share and communicate customer insights through data visualization
Views: 7391 AYLIEN
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning
 
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What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning - SOCIAL MEDIA MINING definition - SOCIAL MEDIA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services). Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens. As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.
Views: 335 The Audiopedia
Data Science - Part XI - Text Analytics
 
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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
Views: 16396 Derek Kane
Intro - Mining Data from Social Media with Python
 
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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 ----- ----- Source Code for tutorials on Youtube: http://bit.ly/2nSQSAT ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh ----- Learn Something New: ------ Learn Something New: http://bit.ly/2zSkzGh
Views: 9152 Sukhvinder Singh
Pseudo Feature Extraction in Social Network Analysis and Text Mining
 
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This is a webinar I delivered as a part of a webinar series entitled "We are all Social Things" organized by the IS department at King Saud University - Female Section The recording started a bit late, but you will be able to follow.
Views: 751 Ibrahim Almosallam
Text Mining - The Context (Part 1)
 
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VokseDigital : Digital Analytics | Marketing Technologies https://voksedigital.com This is first video in the series of 12 videos. In this series we are focusing on gathering data from social media websites and blogs to deliver a better customer insight.
Text Analytics with R | Setting Up the access between R and Twitter | Twitter Data Mining - Part 1
 
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In this text analytics with R tutorial, I've talked about how you can setup the access between R and twitter to fetch the tweets related to the search string. Twitter data is very helpful in understanding the sentiments of people related to a particular topic and helps driving an important decision. Text Analytics with R,Setting up the access between R and Twitter,Twitter Data Mining,Learn text analytics in R,how to get twitter data in r,extracting twitter data in r,connection between twitter and r,access setup between twiiter and R,how to get tweets from twitter in R,analyzing tweets in R,text anlaytics on tweets,how to connect r with twitter,twtter r connection,twitter data in r,social media mining in r,R - Twitter Mining with R,R Twitter API Tutorial
Text Mining Webinar
 
01:22:56
A newer version of this webinar is available at https://www.youtube.com/watch?v=upAwDcw9ra4&t=1870s This is the recording of the KNIME Text Mining Webinar held on October 30, 2013. It covers all the basic steps of text mining using KNIME 2.8 nodes: Document creation, enrichment, transformation, preprocessing, visualization, topic detection, and sentiment analysis. Workflows used in this webinar are available on the KNIME EXAMPLES server under 060_Webinars/TextMiningWebinar .
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