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WordStat - Content Analysis and Text Mining Software
 
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WordStat a content analysis and text mining software from Provalis Research.
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 65604 deltaDNA
Text Mining: NGram Word Frequency in R
 
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Using R, you can see what how often words occur in an aggregated data set. It is often used in business for text mining of notes in tickets as well as customer surveys. Using a Corpus and TermDocumentMatrix in R we can organize the data accordingly to extract the most common word combos. Direct File: https://github.com/ProfessorPitch/ProfessorPitch/blob/master/R/NGram%20Wordcloud.R Software Versions: R 3.3.3 Java = jre1.8.0_171 (64 bit) R Packages: library(NLP) library(tm) library(RColorBrewer) library(wordcloud) library(ggplot2) library(data.table) library(rJava) library(RWeka) library(SnowballC)
Views: 5650 ProfessorPitch
Text mining with Voyant Tools, no R or any other coding required
 
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Please explore free and beautiful Voyant Tools that allow you to perform any text analysis or even mining - word frequency, clouds, co-occurrence (collocations), spider diagrams, context analysis - anything you dreamt of without any prior programming experience or need to buy expensive software. To those interested in reproducing what we've done and further analyzing comments to Indian political articles (dated March-April and January 2016), please use this link to get the ball rolling: http://voyant-tools.org/?corpus=0c17d82dbd8b04baae655f90db84a672 Lastly, creators of the video are eternally grateful to our Big Data class professor, who believed in us and kept us going despite any technical or analytical difficulties.
Views: 7252 Adventuruous Mind
Word Mining is Your Future
 
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Views: 204 Serba Ada
Word Mining   Reinvestimento  Saque  Prova de Pagamento
 
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https://world-mining.net/906147973/
Views: 11 MysteryWEB
How to perform disambiguation of words with WordStat - Content Analysis and Text Mining Software
 
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Learn how to perform disambiguation with WordStat using phrases or rules.
Introduction to the Linguistic Inquiry and Word Count
 
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This 7:20 video introduces the viewer to the software tool known as LIWC ('Luke'), aka the Linguistic Inquiry and Word Count program. This video is supported by the Centre for Human Evolution, Cognition and Culture, and its Cultural Evolution of Religion project, at University of British Columbia. The HECC website has accompanying instructional blog posts about the use of LIWC here http://www.hecc.ubc.ca/cerc/.
Views: 8514 ryantatenichols
Power Bi - Text Mining (Word Frequency)
 
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Addition to my blogpost about Textmining with R and PowerBI
Views: 3360 Sammy Deprez Blog
JMP Text Explorer
 
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Examine your unstructured text data using JMP Text Explorer. Text Explorer includes basic tools for analyzing free text in surveys or engineering notes, for example, and provides an interface for extracting part numbers, words between HTML tags, phone numbers, email addresses, currency and more. Watch this video and try exploring your data in new ways with Text Explorer. Learn more about JMP software: https://www.jmp.com/en_us/home.html Download the JMP free trial: https://jmp.com/trial Buy JMP online: https://jmp.com/buy Join the JMP Community: https://community.jmp.com Read the JMP Blog: https://jmp.com/blog Follow @JMP_software on Twitter: https://twitter.com/JMP_software Follow @JMP_tips on Twitter: https://twitter.com/JMP_tips Sign up to receive the JMP newsletter: https://www.jmp.com/en_us/newsletters/jmp-newswire/subscribe.html
Views: 2240 JMPSoftwareFromSAS
Why You Should Spread The Word Around Tron 2018
 
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Views: 2 Crypto Time
World Mining | Tutorial y Análisis en Español (2018)
 
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Bienvenidos una vez más a BlockChainDP hoy os traigo un nuevo video en este caso he hecho un tutorial de World Mining, una nueva empresa de minado en la nube instalada en Europa, concretamente Irlanda del norte, esta empresa nos ofrece diversos tipos de contratos de minado y un software propio para minar con nuestro ordenador, en este tutorial veremos desde cómo registrarse, cómo comprar un contrato de minado, cómo minar con su software y finalmente hablaré del el resto de herramientas que nos ofrece esta página, además lo voy a hacer desde un punto de vista imparcial ya que no hago contenido patrocinado, así que empecemos! Worldmining Enlace Referido https://world-mining.net/287684057/ BlockchainDP página web oficial https://www.BlockchainDP.com BlockchainDP Discord https://discord.gg/ytRWvxk Información Criptomonedas https://bitcointalk.org/ https://coinmarketcap.com/ https://whattomine.com/ RIG Rentabilidad Minado Actual https://whattomine.com/
Views: 818 BlockChainDP
How to easily perform text data content analysis with Excel
 
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Perform complex text analysis with ease. Automatically find unique phrase patterns within text, identify phrase and word frequency, custom latent variable frequency and definition, unique and common words within text phrases, and more. This is data mining made easy. Video Topics: 1) How to insert text content data for analysis 2) Perform qualitative content analysis on sample survey 3) Review text content phrase themes and findings within data 4) Review frequency of words and phrase patterns found within data 5) Label word and phrase patterns found within data
Views: 59983 etableutilities
(Basic) Text Analysis with WORDij
 
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This video shows you how to use WORDij (http://wordij.net) to analyze textual data. I focus a) on word and word pair frequencies, and b) on how to create a semantic network and visualize it using gephi (http://gephi.org).
Views: 1967 Bernhard Rieder
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Views: 37693 Hash Oceans
Easiest way to mine for Bitcoin from your PC (Desktop or Laptop) 2018
 
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Views: 18761 Husham Memar
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Views: 826 fredazip
Topic Detection with Text Mining
 
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Meet the authors of the e-book “From Words To Wisdom”, right here in this webinar on Tuesday May 15, 2018 at 6pm CEST. Displaying words on a scatter plot and analyzing how they relate is just one of the many analytics tasks you can cover with text processing and text mining in KNIME Analytics Platform. We’ve prepared a small taste of what text mining can do for you. Step by step, we’ll build a workflow for topic detection, including text reading, text cleaning, stemming, and visualization, till topic detection. We’ll also cover other useful things you can do with text mining in KNIME. For example, did you know that you can access PDF files or even EPUB Kindle files? Or remove stop words from a dictionary list? That you can stem words in a variety of languages? Or build a word cloud of your preferred politician’s talk? Did you know that you can use Latent Dirichlet Allocation for automatic topic detection? Join us to find out more! Material for this webinar has been extracted from the e-book “From Words to Wisdom” by Vincenzo Tursi and Rosaria Silipo: https://www.knime.com/knimepress/from-words-to-wisdom At the end of the webinar, the authors will be available for a Q&A session. Please submit your questions in advance to: [email protected] This webinar only requires basic knowledge of KNIME Analytics Platform which you can get in chapter one of the KNIME E-Learning Course: https://www.knime.com/knime-introductory-course
Views: 2996 KNIMETV
LIWC Text Analysis Tutorial
 
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In this tutorial, we will go over how to utilize LIWC software (http://liwc.wpengine.com/) to conduct content and sentiment analysis on your very own documents. This is Part 2/2 of our video series showing how to scrape and analyze reddit comment threads. For Part 1, follow the link: https://www.youtube.com/watch?v=yexxcrPC7U8&feature=youtu.be
Views: 5963 I Johar
WORD CLOUD TABLEAU TUTORIAL
 
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Become a cutting-edge TABLEAU expert in as little as 8 HOURS with our newest data science online course — now 95% off. Dive into all that Tableau 2018 has to offer and take your data science career to whole new heights with “Tableau 2018: Hands-On Tableau Training For Data Science” — currently rated 4.6/5 on Udemy. Learn by doing with step-by-step lectures, real-life data analytics exercises and quizzes. ================================================= 95% OFF — A limited time, YouTube ONLY offer! Enroll today ==&gt https://www.udemy.com/tableau-2018/?couponCode=YOUTUBE95 ================================================= Here’s what some of our bright students have to say about the course! “I took almost every course from [instructor] Kirill and his team. This is one of the best ones so far. Examples and pace of the course are perfect in my opinion.” — Philipp S. “Intuitive guidance about how to interpret data and present it in a way that is easily comprehensible.” — Khushwinder B. Join over 523,000 data science lovers and professionals in taking your skills to the next level. Leverage opportunities for you or key decision makers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks. Master everything there is to know about Tableau in 2018 ======================================== - Getting started - Tableau basics - Time series, aggregation and filters - Maps, scatterplots and launching your first dashboard - Joining and blending data - Creating dual axis charts - Table calculations, advanced dashboards, storytelling - Advanced data preparation - Clusters, custom territories, design features - What’s new in Tableau 2018 Learn on-the-go and at your convenience — via mobile, desktop, and TV — in a 70-lecture course that breaks down topics into fun and engaging videos while covering all the Tableau 2018 functions you’ll ever need. And don’t hesitate to start from the beginning, or skip ahead with our independent modules. Learn how to make Word Cloud in Tableau through this amazing tutorial! Get the dataset and completed Tableau workbook here: https://www.superdatascience.com/yt-tableau-custom-charts-series/ A visualisation method that displays how frequently words appear in a given body of text, by making the size of each word proportional to its frequency. All the words are then arranged in a cluster or cloud of words. Alternatively, the words can also be arranged in any format: horizontal lines, columns or within a shape. Word Clouds can also be used to display words that have meta-data assigned to them. For example, in a Word Cloud of all the World's countries, population could be assigned to each country's name to determine its size. Colour used on Word Clouds is usually meaningless and is primarily aesthetic, but it can be used to categorise words or to display another data variable. Typically, Word Clouds are used on websites or blogs to depict keyword or tag usage. Word Clouds can also be used to compare two different bodies of text together. To stay up to date with our latest videos make sure to subscribe to SuperDataScience YouTube channel!
Views: 15223 SuperDataScience
Coding Text Using Microsoft Word
 
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Describes how to use Word's comment feature to code text and then extract text segments to a table for analysis. The video uses a modified version of a Word macro available at http://www.thedoctools.com/index.php?show=mt_comments_extract
Views: 86810 Harold Peach
Analyzing Text Data with R on Windows
 
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Provides introduction to text mining with r on a Windows computer. Text analytics related topics include: - reading txt or csv file - cleaning of text data - creating term document matrix - making wordcloud and barplots. 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: 9340 Bharatendra Rai
World mining best mining platform review on 2018 Par day ultimate income for online
 
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Views: 31 HALLO WORLD
Blockchain 101 Ep 25 - How is mining done
 
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Before the rise of blockchain technology, “miner” usually refers to coal miners. The image of a hardworking man with his body covered in soot comes to mind. After the birth of blockchain, the word “miner” takes on a new meaning those who mine cryptocurrencies. Different from the traditional miner, cryptocurrency miners have much to offer in the world of technology. Their main responsibilities are to validate and process transactions. It’s fairly simple to become a miner if you are interested. Purchase a dedicated mining rig. Download a mining software and you can get right to it! You don’t have to physically code as it is all automated and your rig can mine on its own. All you need is to ensure a stable electricity supply and internet connection. Watch the video here! Huobi Pro will post a question in our Tweet via our Twitter account (https://twitter.com/Huobi_Pro) each time we put up an episode of Blockchain 101 post on our Twitter account, we will randomly select 20 people who answered in the format [UIDxxxxxxxx, correct answer] and retweet our post will be entitled to 1 HT each. Note that the UID entered MUST be a valid Huobi registered account. What are you waiting for? Fastest fingers first! Grab your HT now!
Views: 681 Huobi Global
RX Vega 56/64 Mining Software Guide - Step by Step!
 
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Welcome back Squad! In this video I share my configuration for my RX Vega 56s and my 64s. I also have word documents made that you can follow step by step to replicate the same performance! Each guide walks you through how to configure, flash, optimize and automate your video cards for mining. RX Vega 56 Software Guide: https://goo.gl/keznDh Alternate link: https://drive.google.com/open?id=11f9JkmAcjsC7w5mVUnb6AGtLRr75VrFK RX Vega 56 Software Package: https://goo.gl/rysW5R Alternate link: https://drive.google.com/open?id=1y0aJppzZMmwxyA6wcuCzKKSISTwgSbWk ---------- RX Vega 64 Software Guide: https://goo.gl/pmPh8J RX Vega 64 Software Package: https://goo.gl/XoAQTw Quick Links: 00:43 - RX Vega 56 Guide 12:20 - RX Vega 64 Guide 15:07 - Performance Test 17:07 - Profit Breakdown Please let me know if any information requires adjustment and I will upload revisions to this document ^_^. Thank you guys for checking this out and as always, Stay Regular ----- Here are the Exchanges I use! QuadrigaCX: https://www.quadrigacx.com/?ref=tono4thdtbjummkpszeaqz7t Cryptopia: https://www.cryptopia.co.nz/Register?referrer=log1466 Binance: https://www.binance.com/?ref=12259869
Views: 3835 That Regular Guy
Making First Bitbond payment- Tutorial and few word on future plans
 
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Text Mining in R  Term Frequency & Word Clouds
 
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Text Mining in R Term Frequency & Word Clouds
Views: 4235 finlearn
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: 23689 DataCamp
World Mining How to Earn $100 Daily | How to Make $100 Daily Bitcoin | Tutorial {English}
 
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Text Mining (part 3)  -  Sentiment Analysis and Wordcloud in R (single document)
 
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Sentiment Analysis Implementation and Wordcloud. Find the terms here: http://ptrckprry.com/course/ssd/data/positive-words.txt http://ptrckprry.com/course/ssd/data/negative-words.txt
Views: 21812 Jalayer Academy
NiceHash The Mining Service Got Hacked And Lost 4,450 Bitcoins Worth 60 Million Dollars
 
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The new howmuchbitcoin channel forum: http://www.idanabada.com/bitcoin Terrible news, the mining software and service NiceHash got hacked today and lost almost 4,450 bitcoins today. This is a terrible news for the mining community. Let us know how did that effect you. Check updates about NiceHash Here - https://www.nicehash.com/ Visit Our http://www.bitcoinmerch.com/ store Visit Our Forum http://www.idanabada.com/ Support my channel on Patreon to help spread the crypto word - https://www.patreon.com/howmuchbitcoin
Views: 19947 HowMuchBitcoin
Ryzen 7 1700X Turning Profits Mining Cryptonite
 
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Views: 37157 HowMuchBitcoin
BITCOIN MINER
 
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Word´s Most Efficient Miner A Bitcoin miner is a computer specifically designed to solve problems according to the proof of work algorithm. Currently, highly specialized chips called ASICs, Application Specific Integrated Circuits, are used as Bitcoin miners.
Views: 1079 Cincobe Publicidad
2. Text Mining Webinar - Create a Document
 
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This is the second part of the text Mining Webinar recorded on October 30 2013 (https://www.youtube.com/edit?o=U&video_id=tY7vpTLYlIg). This part describes all ways and nodes to create a Document data in KNIME, from reading documents from a folder (PDF, SDML,TXT, WORD DOC, RSS Feeds, etc...).
Views: 3522 KNIMETV
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Views: 360 Technical Universe
OnePlus5 Mobile Cryptocurrency Mining
 
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This is just a short video show the Hash rate that you can get with oneplus 5. The link for the software to mine cryptocurrency:- https://minergate.com/a/793c782a3b004007568cf3ba
Views: 748 Tech2Tap
Burstcoin Mining - Low Disk Space Notification Fix
 
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This video guides you through creating a new registry entry to prevent windows from popping up constantly saying you're running low on disk space. 1) Open Registry Editor 2) Goto HKEY_CURRENT_USER\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\Explorer 3) Create new D-Word 4) Name it "NoLowDiskSpaceChecks" 5) Modify the Value to 1 All done, no more annoying notifications.
Views: 277 DSEC UK
CNN Money : Bitcoins jump 1000% this year and still climbing.
 
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Views: 85 One BTC Chris
Sentiment Analysis in 4 Minutes
 
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Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 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
Views: 94482 Siraj Raval
R tutorial: Getting started with text mining?
 
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Learn more about text mining with R: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Boom, we’re back! You used bag of words text mining to make the frequent words plot. You can tell you used bag of words and not semantic parsing because you didn’t make a plot with only proper nouns. The function didn’t care about word type. In this section we are going to build our first corpus from 1000 tweets mentioning coffee. A corpus is a collection of documents. In this case, you use read.csv to bring in the file and create coffee_tweets from the text column. coffee_tweets isn’t a corpus yet though. You have to specify it as your text source so the tm package can then change its class to corpus. There are many ways to specify the source or sources for your corpora. In this next section, you will build a corpus from both a vector and a data frame because they are both pretty common.
Views: 4745 DataCamp
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: 75764 Linguamatics
Text mining using Excel, Semantria, and Tableau
 
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In less than 5 minutes, we take 20,000 tweets from Datasift, perform text mining through the Lexalytics/Semantria Excel Plugin, import the results into Tableau, and start visualizing cool stuff. (This process applies to Tableau version 8.2).
Views: 18490 Lexalytics
R - Twitter Mining with R (part 2) create WordCloud from Tweets
 
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Twitter Mining with R. In (part 2) we searchTwitter for some tweets related to the 2015 earthquake in Nepal. After cleaning the text with the tm package we create a wordcloud that takes our 500 tweets and gives a highly informative and beautiful visualization of what people are tweeting on the subject. In (part 1) we set up the Authorization with the twitter API so that we can begin searching and retrieving Tweets. Note: (part 1) https://www.youtube.com/watch?v=lT4Kosc_ers&index=25&list=PLjPbBibKHH18I0mDb_H4uP3egypHIsvMn is essential and you will not get far in (part 2) of Twitter Mining with R if you have not done this. 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: 47486 Jalayer Academy
Best Litecoin Mining Robot 2017,2018 free LTC Mining bot Software
 
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World Mining Full Tutorial | How to Earn Free Bitcoin | World Mining Reviews
 
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Weka Tutorial 31: Document Classification 1 (Application)
 
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This tutorial demonstrates the use of an unsupervised attribute filter called StringtoWordVector. This filter converts the strings (texts) of your document into a vector of words. Then using the word vectors, you can develop a classification model. The next tutorial shows the actual work involved in document classification
Views: 25600 Rushdi Shams

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