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Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 65756 Well Academy
What is Text Mining?
 
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An introduction to the basics of text and data mining. To learn more about text mining, view the video "How does Text Mining Work?" here: https://youtu.be/xxqrIZyKKuk
Views: 57498 Elsevier
INTRODUCTION TO TEXT MINING IN HINDI
 
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Please Support LearnEveryone Channel,Small Contribution shall help us to put more content for free: Patreon - https://www.patreon.com/LearnEveryone ------------------------------------------------- find relevant notes at-https://viden.io/
Views: 10574 LearnEveryone
How does Text Mining Work?
 
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Understand the basics of how text and data mining works and how it is used to help advance science and medicine. To learn what text mining is, view the video "What is Text Mining?" here: https://youtu.be/I3cjbB38Z4A
Views: 14694 Elsevier
What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning, definition & explanation
 
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What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning - TEXT MINING definition - TEXT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining" in 2004 to describe "text analytics." The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s, notably life-sciences research and government intelligence. The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80 percent of business-relevant information originates in unstructured form, primarily text. These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing.
Views: 2881 The Audiopedia
Web Mining - Tutorial
 
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Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
What is Text Mining?
 
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The introduction of Text Mining-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 3055 Jian Cui
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: 29105 DataCamp
What is the difference between keyword search and text mining?
 
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This is a brief introduction to the difference between keyword search and text mining. Find out more from the leader in natural language based text mining solutions, by visiting our blog and website: http://www.linguamatics.com/blog Following us on social media: Twitter: www.twitter.com/linguamatics LinkedIn: www.linkedin.com/company/linguamatics Facebook: www.facebook.com/Linguamatics YouTube: https://www.youtube.com/user/Linguama... You can contact us with questions at enquiries @ linguamatics.com
Views: 605 Linguamatics
What is Web Mining
 
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Views: 15285 TechGig
Text Mining (How to Data Mine)
 
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Statgraphics 18 is used to analyze 9 famous speeches. It uses the tm Text Mining library in R to construct a document-term matrix, which is then used to create a wordcloud. A comparison of 2 speeches is also shown using a tornado/bufferfly plot. For more examples and information on this procedure, please visit our website: http://www.statgraphics.com/data-mining.
Views: 434 Statgraphics
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
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** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 58418 edureka!
Text Mining in Publishing
 
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TEXT MINING AND SCHOLARLY PUBLISHING: This short video by John Bond of Riverwinds Consulting discusses Text Mining and the Scholarly Publishing Industry. MORE VIDEOS on TEXT MINING and Scholarly Publishing can be found at: https://www.youtube.com/playlist?list=PLqkE49N6nq3jY125di1g8UDADCMvCY1zk FIND OUT more about John Bond and his publishing consulting practice at www.RiverwindsConsulting.com SEND IDEAS for John to discuss on Publishing Defined. Email him at [email protected] or see http://www.PublishingDefined.com CONNECT Twitter: https://twitter.com/JohnHBond LinkedIn: https://www.linkedin.com/in/johnbondnj Google+: https://plus.google.com/u/0/113338584717955505192 Goodreads: https://www.goodreads.com/user/show/51052703-john-bond YouTube: https://www.youtube.com/c/JohnBond BOOKS by John Bond: The Story of You: http://www.booksbyjohnbond.com/the-story-of-you/about-the-book/ You Can Write and Publish a Book: http://www.booksbyjohnbond.com/you-can-write-and-publish-a-book/about-the-book/ TRANSCRIPT: Hi there. I am John Bond from Riverwinds Consulting and this is Publishing Defined. Today I am going to discuss text mining as it relates to scholarly publishing. Text mining also goes by the phrase text data mining or text analytics. Text mining in scholarly publishing is the process of deriving high-quality information from peer reviewed articles and other content. It does this by processing large amounts of information and looking for patterns within the data, and then evaluating and interpreting the results. Text mining is most beneficial to researchers or other power users of technical content. It is very different from a keyword search such that you might perform with Google. A key word search likely produces thousands of web links with no uniformity in the results and certainly no ability to draw meaningful conclusions. An example: let’s say you are researching bladder cancer in men and you are looking for specific biomarkers for other disease states. You probably don’t have the time to review all the literature you might find through a search at PubMed. Text mining will review the available literature. It understands the parts of speech (nouns, verbs), recognizes abbreviations, takes term frequency into account, and other natural language processes. It will filter through all the content, extracts relevant facts, spot patterns, and provides the researcher with a more condensed set of results and statements than a literature search or a cursory review of abstracts ever could. It knows bladder cancer is a disease state. It knows, in this instance, to look for men as opposed to women. It understands what a biomarker is and how to apply this term to other disease states. It understands bladder cancer is a phrase and not being used as two separate terms. Text mining software involves high level programming and such concepts as word frequency distribution, pattern recognition, information extraction, and natural language processing as well as other programming concepts well beyond the scope of this video. The overall goal is to turn text into data for analysis and thereby help to draw conclusions. However, the results of text mining in and of themselves is not the end product, just part of the process. Individual text mining tools or enterprise level ones have become more common with researchers, librarians, and large for profit and not for profit organizations, and they will only grow. Aside from a text mining tool, an application is also necessary to check that the content being mined is licensed and to provide appropriate links to the content. Text mining is important to publishers or any group that holds large stores of full text articles or databases because this information as a whole has greater value than each individual part. Text mining can help extract that value. A key point for publishers is that the text mining tool and its user, such as a researcher, needs to have access to the content either by it being open access, through a subscription, or through a purchase. Subscription publishers see revenue when content is accessed or purchased. All publishers see article downloads and page views from text mining efforts. Either way, text mining as a tool in research, in medicine, in pharmaceutical R&D will only continue to grow in importance. Well that’s it. Please subscribe to my YouTube channel or click on the playlist to see more videos about text mining in scholarly publishing. And make comments below or email me with questions. Thank so much and take care.
Views: 318 John Bond
Building Smarter Web Applications with Text Mining
 
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Bashar Al-Fallouji http://2014.osdc.com.au/presentation/19-building-smarter-web-applications-text-mining http://2014.osdc.com.au/presentation/19-building-smarter-web-applications-text-mining
Views: 103 OSDC Australia
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1640 Kakoli Bandyopadhyay
Introduction to text mining with Voyant
 
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In this introduction to text mining with Voyant I cover: 1) Data cleaning (text editors, Notepad++ and Sublime Text) 2) Loading your text into Voyant 3) Expectations, what Voyant can and cannot do 4) Working with common visualization tools and making possible connections 5) Exporting visualizations
Text Mining for Beginners
 
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This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 79131 Linguamatics
misy 4390 chapter 5 on text and web mining.flv
 
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lecture on text mining and web mining
Views: 2026 Kakoli Bandyopadhyay
"Data Science" What Is Text Mining ? | Applications Of Text Mining And Clustering | Training -ExcelR
 
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What Is Text Mining ? | Applications Of Text Mining And Clustering #DataScience #TextMining #Clustering #TextMining In Datascience #Applications of Textmining #training #2018 Text Mining, Hmm before we dive into the water we need to make sure that we are ready. So in the same way before going into deep we should know what is text-mining and it`s applications? Daily a new data is being generated and mostly majority of data is unstructured. To convert/transform the unstructured data (unstructured text data) into structured, then the unstructured text data is to set for analysis using text-mining to get new generated information. Like we get daily 1. Call transcripts, 2. Emails that we sent to customer service 3. Social Media outreach (Facebook, twitter, Instagram and many more) 4. Speech transcripts 5. Filed agents, sales people 6. Interviews and survey`s The process of getting high quality of information deriving from the text data is text –mining. To examine the large amount of text data/Written data sources to generate new information. This quality information I typically derived through devising of patters and trends such as statistical pattern learning. Clustering in data mining is gathering set of abstract data and aggregating them based on their similarities. Here are some of the applications of text mining and clustering are: 1. Text categorization into particular domains 2. Organizing a set of documents automatically by text Clustering. 3. Identifying and extracting subject information in documents. In other words-sentiment analysis. 4. Extracting entity/concepts which can identify people, places, organisations and other entities. 5. Learning relations between named entities. In this video you will learn about 1. Text Mining and use of Clustering 2. Applications of Text Mining 3. What is Word Cloud? SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/excelr-solutions/ Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Text Mining
 
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None-- Created using PowToon -- Free sign up at http://www.powtoon.com/ . Make your own animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 2447 Soumya shetty
Text Mining Example Using RapidMiner
 
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Explains how text mining can be performed on a set of unstructured data
Views: 16784 Gautam Shah
Text mining: Key concepts and applications
 
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Jee-Hyub Kim and Senay Kafkas from the Literature Services team at EMBL-EBI present this talk on an introduction to text mining and its applications in service provision. The 1st part of this talk focuses on what text mining is and some of the methods and available tools. The 2nd part looks at how to find articles on Europe PMC - a free literature resource for biomedical and health researchers - and how to build your own text mining pipeline (starts at 20:30 mins). The final part gives a nice case study showing how Europe PMC's pipeline was integrated into a new drug target validation platform called Open Targets (previously CTTV) (starts at 38:20 mins). This video is best viewed in full screen mode using Google Chrome.
Text Analytics with R | How to Scrap Website Data for Text Analytics | Web Scrapping in R
 
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In this text analytics with R tutorial, I have talked about how you can scrap website data in R for doing the text analytics. This can automate the process of web analytics so that you are able to see when the new info is coming, you just run the R code and your analytics will be ready. Web scrapping in R is done by using the rvest package. Text analytics with R,how to scrap website data in R,web scraping in R,R web scraping,learn web scraping in R,how to get website data in R,how to fetch web data in R,web scraping with R,web scraping in R tutorial,web scraping in R analytics,web scraping in r rvest,web scraping and r,web scraping regex,web scraping facebook in r,r web scraping rvest,web scraping in R,web scraper with r,web scraping in r pdf,web scraping avec and r,web scraping and r
Text Mining In R | Natural Language Processing | Data Science Certification Training | Edureka
 
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** Data Science Certification using R: https://www.edureka.co/data-science ** In this video on Text Mining In R, we’ll be focusing on the various methodologies used in text mining in order to retrieve useful information from data. The following topics are covered in this session: (01:18) Need for Text Mining (03:56) What Is Text Mining? (05:42) What is NLP? (07:00) Applications of NLP (08:33) Terminologies in NLP (14:09) Demo Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - - - - #textmining #textminingwithr #naturallanguageprocessing #datascience #datasciencetutorial #datasciencewithr #datasciencecourse #datascienceforbeginners #datasciencetraining #datasciencetutorial - - - - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyze Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyze data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies. For online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
Views: 6493 edureka!
Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 14105 Stat Pharm
Text Analytics and Text Mining Explained by OdinText
 
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Text Analytics Explained. Anderson Analytictics, developers of Next Generation Text Analytics software platform OdinText explain Text Analytics and the power of text mining, as well as the difference between first generation text analytics software from IBM SPSS, SAS Text, Attensity and Clarabridge compared to the OdinText Next Generation Text Analytics approach to text and data mining. http://www.OdinText.com
Views: 27471 OdinText
web scraping using python for beginners
 
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Learn Python here: https://courses.learncodeonline.in/learn/Python3-course In this video, we will talk about basics of web scraping using python. This is a video for total beginners, please comment if you want more videos on web scraping fb: https://www.facebook.com/HiteshChoudharyPage homepage: http://www.hiteshChoudhary.com Download LearnCodeOnline.in app from Google play store and Apple App store
Views: 200331 Hitesh Choudhary
Text Mining Tutorials for Beginners | Importance of Text Mining | Data Science Certification -ExcelR
 
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ExcelR: Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Things you will learn in this video 1) What is Text mining? 2) How clustering techniques helps and text data analysis? 3) What is word cloud? 4) Examples for text mining 5) Text mining terminology and pre-processing To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here " https://goo.gl/JTkWXo" SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For K-Means Clustering Tutorial click here https://goo.gl/PYqXRJ For Introduction to Clustering click here Introduction to Clustering | Cluster Analysis #ExcelRSolutions #Textmining #Whatistextmining #Textminingimportance #Wordcloud #DataSciencetutorial #DataScienceforbeginners #DataScienceTraining ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
How NLP text mining works: find knowledge hidden in unstructured data
 
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Connect with us: http://www.linguamatics.com/contact What use is big data if you can't find what you're looking for? Follow: @Linguamatics https://twitter.com/Linguamatics https://www.linkedin.com/company/linguamatics https://www.facebook.com/Linguamatics https://plus.google.com/+Linguamatics https://www.youtube.com/user/Linguamatics/videos In knowledge driven industries such as the life sciences and healthcare, finding the right information quickly from huge volumes of text is crucial in supporting the best business decisions. However, around 80% of available information exists as unstructured text, and conventional keyword searches only retrieve documents, which still have to be read. This is very time consuming, unreliable, and, when important decisions rest on it, costly. Linguamatics’ text mining solution, I2E, uses Natural Language Processing to identify and extract relevant knowledge at least 10 times faster than conventional search, often uncovering insights that would otherwise remain unknown. I2E analyses the meaning of the text using powerful linguistic algorithms, enabling you to ask open questions, find the relevant facts and identify valuable connections. Going beyond simple keywords, I2E can recognise concepts and the different ways the same thing can be expressed, increasing the recall of relevant information. I2E then presents high quality results as structured, actionable knowledge, enabling fast review and analysis, and providing dramatically improved speed to insight. Our market leading software is supported by highly qualified domain experts who work with our customers to ensure successful project outcomes. Text mining for beginners: https://www.youtube.com/watch?v=40QIW9Sr6Io
Views: 17265 Linguamatics
INTRODUCTION TO DATA MINING IN HINDI
 
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Please Support LearnEveryone Channel,Small Contribution shall help us to put more content for free: Patreon - https://www.patreon.com/LearnEveryone ------------------------------------------------- Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 118048 LearnEveryone
Text mining for ontology learning and matching
 
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http://togotv.dbcls.jp/20141117.html NBDC / DBCLS BioHackathon 2014 was held in Tohoku Medical Megabank in Sendai and Taikanso in Matsushima, Miyagi, Japan. Main focus of this BioHackathon is the standardization and utilization of human genome information with Semantic Web technologies in addition to our previous efforts on semantic interoperability and standardization of bioinformatics data and Web services. (read more about the past hackathons...) On the first day of the BioHackathon (Nov. 9), public symposium of the BioHackathon 2014 was held at Tohoku Medical Megabank in Sendai. In this talk, Jung-Jae Kim (Nanyang Technological University, Singapore) makes a presentation entitled "Text mining for ontology learning and matching". (16:09)
Views: 2158 togotv
Introduction to Text Analytics with R: Overview
 
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The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – 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 About the Series 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 if 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 Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz5_y0 Watch the latest video tutorials here: https://hubs.ly/H0hz61V0 See what our past attendees are saying here: https://hubs.ly/H0hz6-S0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 75924 Data Science Dojo
Web Scraping Using PHP - Parse IMDB.com Movies HTML
 
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Using PHP and regular expressions, we're going to parse the movie content of IMDB.com and save all the data in one single array. Web scraping using regex can be very powerful and this video proves it. We account for empty elements by matching groups of HTML blocks, looping through the blocks of matched content and then matching single elements, if they're found from the block. This technique of matching content and web scraping can be used on just about any web site to parse out it's content. ````````````````````````````````````````````````````````````````````````````````````````````` Hey guys, I'm now using Patreon to share improved and updated video lesson material. For a small fee you can access all the downloadable files from this lesson (source code, icons & graphics, cheat sheets) and everything else included in the video from the Patreon page. Additionally, you will get access to ALL Clever Techie videos in HD format with no ads. Thank you so much for supporting Clever Techie :) Download this video's files here: https://www.patreon.com/posts/web-scraping-php-20819046 This download (Patreon unlock) includes: (PHP regex function source code, PHP regex screen shots, PHP regex cheat sheet) + ( You also get access to ALL source code and any downloadable content of ALL Clever Techie videos, as well as access to ALL videos in HD 1080p quality format with all video ads removed! ) ````````````````````````````````````````````````````````````````````````````````````````````` In this web scraping tutorial we’re going to be using regular expressions to parse HTML. This is a more advanced tutorial so you can check out my video on regular expressions before going through this. We’re going to be parsing out the IMDb website, which is an Internet movie database, and I’m going to be using a website called www.regex101.com to test regular expressions against strings to make sure we’re matching them correctly. Because this is an advanced tutorial, I’ll be posting each portion of code and explaining how it works as we walk through it. Directly below is the full source code, but skip down further and I'll walk through each portion of the code. ````````````````````````````````````````````````````````````````````````````````````````````` ( Website ) https://clevertechie.com - PHP, JavaScript, Wordpress, CSS, and HTML tutorials in video and text format with cool looking graphics and diagrams. ( YouTube Channel ) https://www.youtube.com/c/CleverTechieTube ( Google Plus ) https://goo.gl/J71p6f - clever techie video tutorials. ( Facebook ) https://www.facebook.com/CleverTechie/ ( Twitter ) https://twitter.com/theclevertechie
Views: 50692 Clever Techie
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 167978 Timothy DAuria
What is Web Crawler and How Does It Work?
 
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Do you ever wonder what makes the search engines go around? It’s fascinating, isn’t it? The way some mechanism can systematically browse the World Wide Web for web indexing or web spidering… Yes, you are right. A #webcrawler is precisely what we are talking about!!! It’s an #internetbot, also known as #webspider, #automaticindexer, #webrobot or simply #crawler… Guess what else, web crawlers can do? Don’t be surprised, By the functions it performs: 1. update their web content or indices of others sites' web content 2. copy all the visited pages for subsequent processing by a search engine which will index the downloaded pages to provide lightning fast searches 3. automate maintenance tasks on a website, such as checking links or validating HTML code… Do you know the popular open source web crawlers? Here’s the list: 1. Scrapy 2. Apache Nutch 3. Heritrix 4. HTTack How the web crawler works : Enough of the theory, let’s jump right into How a web crawler works: 1. Select a starting seed URL or URLs 2. Add it to the frontier 3. Now pick the URL from the frontier 4. Fetch the web-page corresponding to that URL 5. Parse that web-page to find new URL links 6. Add all the newly found URLs into the frontier 7. Go to step 3 and reiterate till the frontier is empty Did you notice what’s happening here? Wonderful, isn’t it? It’s amazing how search engines work, websites update their content or do their maintenance tasks But… What’s more amazing is the way web crawlers make it happen… Don’t you think so??? for more info about web crawler : http://www.prowebscraper.com/blog/50-best-open-source-web-crawlers/ Follow us on Twitter! https://twitter.com/Prowebscraper
Views: 2128 ProWebScraper
Mining Web Data for Public Health
 
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Recent years have seen the adoption of new Web data sources in a wide range of health areas. Of all areas, public health applications in behavioral medicine have the most potential to change how we conduct research, opening up exciting new opportunities. Fundamentally, behavioral medicine requires understanding how people make health decisions: what influences their decision, how they weigh information, and how social connections impact decisions. Web data sources provide new opportunities for studying these questions. Answering these questions often requires new data mining methods. In this talk, I will present multi-dimensional topic models of text which jointly capture topic and other aspects of text. We describe Factorial Latent Dirichlet Allocation, a multi-dimensional model in which a document is influenced by K different factors, and each word token depends on a K-dimensional vector of latent variables. I will demonstrate the advantages of this model in the application of mining drug experiences from web forums.
Views: 136 Microsoft Research
Brian Carter: Lifecycle of Web Text Mining: Scrape to Sense
 
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Pillreports.net is an on-line database of reviews of Ecstasy pills. In consumer theory illicit drugs are experience goods, in that the contents are not known until the time of consumption. Websites like Pillreports.net, may be viewed as an attempt to bridge that gap, as well as highlighting instances, where a particular pill is producing undesirable effects. This talk will present the experiences and insights from a text mining project using data scraped from the Pillreports.net site.The setting up and the benefits, ease of using BeautifulSoup package and pymnogo to store the data in MongoDB will be outlined.A brief overview of some interesting parts of data cleansing will be detailed.Insights and understanding of the data gained from applying classification and clustering techniques will be outlined. In particular visualizations of decision boundaries in classification using "most important variables". Similarly visualizations of PCA projections for understanding cluster separation will be detailed to illustrate cluster separation. The talk will be presented in the iPython notebook and all relevant datasets and code will be supplied. Python Packages Used: (bs4, matplotlib, nltk, numpy, pandas, re, seaborn, sklearn, scipy, urllib2) Brian Carter
Views: 1195 PyData
Digital Text Mining
 
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Matthew Jockers, University of Nebraska-Lincoln assistant professor of English, combines computer programming with digital text-mining to produce deep thematic, stylistic analyses of literary works throughout history -- an intensely data-driven process he calls macroanalysis. It's opening up new methods for literary theorists to study literature. http://research.unl.edu/annualreport/2013/pioneering-new-era-for-literary-scholarship/ http://research.unl.edu/
Introduction to Text Analytics with R: Cosine Similarity
 
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Cosine Similarity includes specific coverage of: – How cosine similarity is used to measure similarity between documents in vector space. – The mathematics behind cosine similarity. – Using cosine similarity in text analytics feature engineering. – Evaluation of the effectiveness of the cosine similarity feature. The data and R code used in this series is available via the public GitHub here About the Series This data science tutorial is an Introduction to Text Analytics with R. As exemplified by the popularity of blogging and social media, textual data if 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 The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hD5gf0 Watch the latest video tutorials here: https://hubs.ly/H0hD5Pk0 See what our past attendees are saying here: https://hubs.ly/H0hD5hd0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 11942 Data Science Dojo
Twitter Text Mining with Orange 3
 
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A simple example in using Orange 3 to mining texts from Twitter. Notice that collecting data and processing tweet profiles may take 1 minute or more for 500 corpus(es). This video also recorded common mistake in using Twitter widget which is not disabling "Collect result" option if you want a fresh dataset.
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 286001 Siraj Raval
Introduction to Text Analysis with NVivo 11 for Windows
 
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It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share. http://www.qsrinternational.com
Views: 151104 NVivo by QSR
#FixCopyright:  Copyright & Research - Text & Data Mining (TDM) Explained
 
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Read our blog post analysing the European Commission's (EC) text and data mining (TDM) exception and providing recommendations on how to improve it: http://bit.ly/2cE60sp Copy (short for Copyright) explains what text and data mining (TDM) is all about, and what hurdles researchers are currently facing. We also have a blog post on the TDM bits in the EC's Impact Assessment accompanying the proposal: http://bit.ly/2du9sYe Read more about the EC's copyright reform proposals in general: http://bit.ly/2cvAh0a
Views: 3429 FixCopyright
PHD RESEARCH TOPIC IN TEXT MINING
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-contextaware-computing/
Views: 747 PhDprojects. org
What is WEB CONTENT? What doe WEB CONTENT mean? WEB CONTENT meaning & explanation
 
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What is WEB CONTENT? What doe WEB CONTENT mean? WEB CONTENT meaning - WEB CONTENT definition - WEB CONTENT 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 Web content is the textual, visual, or aural content that is encountered as part of the user experience on websites. It may include—among other things—text, images, sounds, videos, and animations. In Information Architecture for the World Wide Web, Lou Rosenfeld and Peter Morville write, "We define content broadly as 'the stuff in your Web site.' This may include documents, data, applications, e-services, images, audio and video files, personal Web pages, archived e-mail messages, and more. And we include future stuff as well as present stuff." While the Internet began with a U.S. Government research project in the late 1950s, the web in its present form did not appear on the Internet until after Tim Berners-Lee and his colleagues at the European laboratory (CERN) proposed the concept of linking documents with hypertext. But it was not until Mosaic, the forerunner of the famous Netscape Navigator, appeared that the Internet become more than a file serving system. The use of hypertext, hyperlinks, and a page-based model of sharing information, introduced with Mosaic and later Netscape, helped to define web content, and the formation of websites. Today, we largely categorize websites as being a particular type of website according to the content a website contains. Web content is dominated by the "page" concept, its beginnings in an academic setting, and in a setting dominated by type-written pages, the idea of the web was to link directly from one academic paper to another academic paper. This was a completely revolutionary idea in the late 1980s and early 1990s when the best a link could be made was to cite a reference in the midst of a type written paper and name that reference either at the bottom of the page or on the last page of the academic paper. When it was possible for any person to write and own a Mosaic page, the concept of a "home page" blurred the idea of a page. It was possible for anyone to own a "Web page" or a "home page" which in many cases the website contained many physical pages in spite of being called "a page". People often cited their "home page" to provide credentials, links to anything that a person supported, or any other individual content a person wanted to publish. Even though we may embed various protocols within web pages, the "web page" composed of "HTML" (or some variation) content is still the dominant way whereby we share content. And while there are many web pages with localized proprietary structure (most usually, business websites), many millions of websites abound that are structured according to a common core idea. Blogs are a type of website that contain mainly web pages authored in HTML (although the blogger may be totally unaware that the web pages are composed using HTML due to the blogging tool that may be in use). Millions of people use blogs online; a blog is now the new "home page", that is, a place where a persona can reveal personal information, and/or build a concept as to who this persona is. Even though a blog may be written for other purposes, such as promoting a business, the core of a blog is the fact that it is written by a "person" and that person reveals information from her/his perspective. Blogs have become a very powerful weapon used by content marketers who desire to increase their site's traffic, as well as, rank in the search engine result pages (SERPs). In fact, new research from Technorati shows that blogs now outrank social networks for consumer influence (Technorati’s 2013 Digital Influence Report data).
Views: 626 The Audiopedia
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
 
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Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. To learn more about Quantopian, visit http://www.quantopian.com. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 2061 Quantopian