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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: 7741 Adventuruous Mind
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: 35158 edureka!
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 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 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/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: 68609 Data Science Dojo
QDA Miner Lite - Free Qualitative Data Analysis Software
 
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In this video, we are going to show you how to use QDA Miner Lite to code and analyze your documents and images. QDA Miner Lite is a free qualitative data analysis software made by Provalis Research.
Text Analytics Software, What Is It and Why is It Worth $1.8 Billion?
 
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Best binary Options broker: http://bit.ly/1zS1i44 looks at text analytics software and why IBM bought Netezza for $1.7 billion recently. Text mining or text digging has been around for years, so why is it so valuable now? Check out this video on data mining to see..
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: 346 Statgraphics
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 155400 Siraj Raval
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 449383 sentdex
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: 164743 Timothy DAuria
SAS® Text Analytics Software Demo
 
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http://www.sas.com/en_us/software/analytics/text-miner.html SAS Text Analytics help companies address big data issues that arise from unstructured content by applying linguistic rules and statistical methods. SAS TEXT MINER Get faster, deeper insight from unstructured data. Why limit yourself to analyzing legacy data? Our text mining software lets you easily analyze text data from the web, comment fields, books and other text sources. Discover new information, topics and term relationships that deepen your understanding. And add what you learn to your models to improve lift and performance. Benefits: * Improve model performance. * Add subject-matter expertise. * Automatically know more. * Determine what's hot and what's not. LEARN MORE ABOUT SAS TEXT MINER http://www.sas.com/en_us/software/analytics/text-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss To learn more about SAS Text Analytics, visit http://www.sas.com/textanalytics
Views: 24583 SAS Software
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: 135334 NVivo by QSR
Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka
 
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** Natural Language Processing Using Python: https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry. NLP Tutorial : https://www.youtube.com/watch?v=05ONoGfmKvA Subscribe to our channel to get video updates. Hit the subscribe button above. ------------------------------------------------------------------------------------------------------- #NLPin10minutes #NLPtutorial #NLPtraining #Edureka 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 learned 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: 37505 edureka!
Text mining using rapidminer
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mobile-cloud-computing/
Views: 691 PHD Projects
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: 16394 Linguamatics
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: 2614 Manolis Maragoudakis
How To Analyze Your Customer Reviews with Text Analysis - Data Crunch
 
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Add in-demand data science skills to your resume: https://www.thelead.io/data-science-360/ If you run a business or organizations, it's important to know what your customers are saying about you - whether if its a form of a blog, social media post, review or comment. This is where text mining and text analysis comes into play. Data scientists build text mining algorithms to mine texts from customers and map out a word cloud to understand their customers. Dr. Lau shows us how to do text analysis in this Data Crunch episode. Text analysis data files for this episode: https://goo.gl/Y5YwRH Google Alerts: https://www.google.com.my/alerts Brandwatch: https://www.brandwatch.com/ =============== Where to follow and learn more from LEAD: Website: https://www.thelead.io Facebook: https://www.facebook.com/thelead.io/ Instagram: https://www.instagram.com/theleadio/ ================ LEAD is an institute in Malaysia, where we provide courses in Data Science, Full Stack Web Development, Digital Marketing & Business, for individuals and corporates — so they can find better careers or to build successful businesses. We teach career-ready skills that our students can use right away in their jobs or find a job. Rather than taking years to learn and master a subject, we have designed our courses to shortcut our students to be competent in the workspace. So we gathered a group of experts in their fields, to teach and mentor our students. Collectively, our 15+ years in technology mentoring means you’ll get real insights & strategies from the best developers, digital marketers, and data scientists.
Views: 330 LEAD
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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 100213 Siraj Raval
Performing Sentiment Analysis
 
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Ever wonder how to analyze people’s words to gauge sentiment? In this video, we’ll show you how! You’ll learn: - How to gather sentiment data - How to clean and structure it - How to perform the analysis in Tableau
Views: 8965 Tableau Software
Understanding Text using Cognitive Services
 
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You will learn how to get started analyzing text! We’ll show you how to sign up for a cognitive service, and the power of Text Analytics, Entity Linking and Bing Entity Search.
Views: 616 Microsoft Developer
INTRODUCTION TO TEXT MINING IN HINDI
 
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find relevant notes at-https://viden.io/
Views: 8693 LearnEveryone
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: 26753 OdinText
Text Mining (part 1)  -  Import Text into R (single document)
 
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Text Mining with R. Import a single document into R.
Views: 19673 Jalayer Academy
Getting Started with Orange 16: Text Preprocessing
 
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How to work with text in Orange, perform text preprocessing and create your own custom stopword list. For more information on text preprocessing, read the blog: [Text Preprocessing] https://blog.biolab.si/2017/06/19/text-preprocessing/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 19064 Orange Data Mining
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: 61138 etableutilities
Text Analysis in Power BI with Cognitive services with Leila Etaati
 
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Abstract: Data that we collected always is not about numbers and structured data. In any organization, there is a need to analyze the text data such as customer comments, extract the primary purpose of a call from its scripts, detect the language of customer feedback and translate it and so forth. To address this issue, Microsoft Cognitive Services provides a set of APIs, SDKs, and services available to developers to do text analysis without writing R or Python codes. In this session, I will explain what is text analysis such as sentiment analysis, key phrase extraction, Language detection and so forth. Next, the process of text analysis in Power BI using cognitive services will be demonstrated. Follow us on Twitter - https://twitter.com/mspowerbi More questions? Try asking the Power BI Community @ https://community.powerbi.com/
Views: 8841 Microsoft Power BI
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
How to build a Text Mining Platform
 
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Tiger Zhang & Lutz Finger on Text Mining Today more than ever before, we have access raw data in the form of texts. Businesses around the world store text discussions from their market research, customer care discussions, or brand relevant conversation on social media. While it is clear that texts contain valuable information, it is often less clear on how best texts can be analyzed at scale. In this class, we will share how we at LinkedIn built a scalable text-mining platform to uncover insights from text data. We will focus on two important components: THEME DISCOVERY of new content and how to CLASSIFY existing text. Using both features, we can detect emerging trends within reviews, customer care discussions and market research data. You will learn: THEME DISCOVERY - information extraction Theme recognition is a highly complex task due to the multi-facetted nature of our language. Theme Recognition (without requiring manual reviews) is, however, the main component of any text-mining platform. We will introduce an innovation in information extraction using part of speech tagging (currently patent pending) to uncover themes within textual data. TEXT CLASSIFICATIONS - Supervised Machine Learning Another important component of our NLP platform is the ability to classify text via supervised machine learning algorithms such as support vector machine (SVM). The ability to classify serves many business use-cases ranging from sentiment analytics to product identification. You will learn in our talk how to cater to those different requirements via a flexible platform setup. VALUE of DATA - Member Feedback The combined ability of Themes Discovery (new content and ideas) as well as Classifications (standard measure) creates a very effective framework to get business insights out of text data. We will demonstrate this on the use case of classifying and responding to member feedback.
Views: 12766 Lutz Finger
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: 66937 deltaDNA
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: 12706 Stat Pharm
Sentiment Analysis in R | Sentiment Analysis of Twitter Data | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Sentiment Analysis Tutorial shall give you a clear understanding as to how a Sentiment Analysis machine learning algorithm works in R. Towards the end, we will be streaming data from Twitter and will do a comparison between two football teams - Barcelona and Real Madrid (El Clasico Sentiment Analysis) Below are the topics covered in this tutorial: 1) What is Machine Learning? 2) Why Sentiment Analysis? 3) What is Sentiment Analysis? 4) How Sentiment Analysis works? 5) Sentiment Analysis - El Clasico Demo 6) Sentiment Analysis - Use Cases Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #SentimentAnalysis #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling 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. Analyse 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. Analyse 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 more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). 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 Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 31085 edureka!
Rapidminer text mining tutorial
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topics-wireless-communication/
Views: 617 PHD Projects
Sentiment Analysis in Excel
 
12:29
Recorded with http://screencast-o-matic.com
Views: 8875 marketingprofessor
Rapidminer text analytics
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-vehicular-adhoc-network/
Views: 38 PHD Projects
How to create a text mining algorithm with Python
 
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In this talk, we'll cover how use Python to create a text mining algorithm that performs TF IDF analysis to find similar text phrases. We'll also discuss text cleanup techniques, such as stop word removal and stemming. About the speaker: Taylor Steinberg is a software engineer at Premier, Inc focused on data science and machine learning.
Views: 4634 Adam Steinberg
Unboxing Six Open Source Annotation Tools - episode C01
 
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Think of this as an unboxing video for annotation software - this is the first time I've tried running any of this software. Don't expect any good demos, I'm just showing you where to find them along with some resources. GATE https://gate.ac.uk/family/ MAE2 https://keighrim.github.io/mae-annotation/ BRAT http://brat.nlplab.org/features.html WebAnno https://webanno.github.io/webanno/ Annis http://corpus-tools.org/annis/ SLATE https://bitbucket.org/dainkaplan/slate/ Works cited: Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications https://smile.amazon.com/Natural-Language-Annotation-Machine-Learning/dp/1449306667/ Overview of Annotation Creation: Processes & Tools. Finlayson, Mark & Erjavec, Tomaž. (2016). https://www.researchgate.net/publication/301847215_Overview_of_Annotation_Creation_Processes_Tools Handbook of Linguistic Annotation. "Collaborative Web-Based Tools for Multi-layer Text Annotation" pp 229-256 https://link.springer.com/chapter/10.1007/978-94-024-0881-2_8 Also, this is the document I meant to show at 14:21 in the video: Annotation Process Management Revisited Dain Kaplan, Ryu Iida, Takenobu Tokunaga Department of Computer Science, Tokyo Institute of Technology http://www.lrec-conf.org/proceedings/lrec2010/pdf/129_Paper.pdf
Views: 1728 Norman Gilmore
Improve your teaching skills using IntenCheck Text Analysis Software
 
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http://www.intentex.com We make effective communication easy for everyone. Intentex is a startup that offers a free next-generation text analysis software which will help you improve your communication and get better results.
Views: 89 Intentex
Consuming REST APIs and Text Mining with RapidMiner
 
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Businesses today rely heavily on REST APIs to create and enrich their data sets, and to improve text mining model performance. Yet working with REST APIs in a data science workflow can be cumbersome and challenging. Plus creating topics that best describe natural text from chats or elsewhere adds insight, but with more complexity.
Views: 1473 RapidMiner, Inc.
6 Best And Free OCR Software 2015 List For Windows 10/7/8/XP/Vista
 
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6 Best, top And Free OCR Software 2015 List For Windows 10/7/8/XP/Vista http://www.softsuggester.com/best-and-free-ocr-software/ Today's video is all about 6 Best And Free Optical character recognition (OCR) software to extract text from images. http://www.softsuggester.com/best-and-free-ocr-software/ What is Optical character recognition (OCR) software? and what does ocr software do? Optical character recognition (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text. It is widely used as a form of data entry from printed paper data records, whether passport documents, invoices, bank statements, computerized receipts, business cards, mail, printouts of static-data, or any suitable documentation. It is a common method of digitizing printed texts so that it can be electronically edited, searched, stored more compactly, displayed on-line, and used in machine processes such as machine translation, text-to-speech, key data and text mining. OCR is a field of research in pattern recognition, artificial intelligence and computer vision. You can learn more about free ocr software at WikiPedia-https://en.wikipedia.org/wiki/Optical_character_recognition this list of 6 Best And Free OCR Software 2015 List For Windows 10/7/8/XP/Vista contains 1)-Microsoft OneNote 2)-Gimagereader 3)-FreeOCR 4)-Boxoft Free OCR 5)-Google Docs 6)-OnlineOCR.Net 6 Best And Free OCR Software 2015 List For Windows 10/7/8/XP/Vista https://youtu.be/8ZVdXrfiy3w
Views: 44583 Simple Tutorials
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: 17765 SuperDataScience
Active Learning with SAS®  Text Miner
 
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The video illustrates key enhancements with the 12.1 release of SAS Text Miner.
Views: 15685 SAS Software
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 456601 Brandon Weinberg
Terms-Gekko text analysis software demo
 
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Terms-Gekko provides practical and easy terms extraction for translators. www.terms-gekko.com
Views: 152 Matti Peteri
Tips, Tricks and Topics in Text Analysis - Bhargav Srinivasa Desikan
 
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PyData LA 2018 Not only is there an abundance of textual data, there is also an abundance of tools help analyse this data - and it is tough to choose the right tool for the right task. In this workshop we will be dealing with the entire text analysis process - this means we'll start with finding data, set up a pipeline to clean our text, annotate it, and then have it ready to do some more advanced analysis. Repo - https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 746 PyData
Why You Should Do Text Analysis in Python (Even if You Don't Want to) - Bhargav Srinivasa Desikan
 
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PyData LA 2018 The explosion in Artificial Intelligence and Machine Learning is unprecedented now - and text analysis is likely the most easily accessible and understandable part of this. And with python, it is crazy easy to do this - python has been used as a parsing language forever, and with the rich set of NLP, ML and Computational Linguistic tools, it's worth doing text analysis even if you don't want to. --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 948 PyData
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.
Medallia Text Analytics
 
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Harnessing the Power of Text Analytics—Right in Your Customer Experience Management Solution This 3-minute video gives a high-level overview of Medallia’s advanced Text Analytics solution capabilities, which are integrated right into Medallia’s CEM platform. medallia.com/category/text-analytics
Views: 2813 Medallia Marketing
Delving into the Q&A network   textmining and graph analysis
 
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Zhen Liang, Xinli Wang Stack Exchange is a Q&A platform where software engineers, scientists, students share knowledge and get questions answered. As users, we are interested in:  - What are heated discussed topics  - How to filtering best answers among all the given answers As developers, we are interested in:  -The problems users are facing and how they can take such information to improve their products and documentation.  Our project addresses such problems by  - Extracting topics out of large amount of posts and the topic distribution of each document  - Predicting the best answers by building a classification model  - Visualizing the “network” of questions, to know what’s the trends and relationships among discussed topics Analytics: - Text mining and feature extraction: NLTK and text mining tools in Python, Spark LDA API with Python and Scala  - Sentiment analysis using AlchemyAPI - Classification algorithms: Random Forest  - Building the graph database in Neo4j  - Visualizations from D3.js toolkit for implementing a visualization to represent the output
Views: 264 Season Liang
Introduction to Text Analytics with R: Our First Model
 
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We are now ready to build our first model in RStudio and to do that, we cover: – Correcting column names derived from tokenization to ensure smooth model training. – Using caret to set up stratified cross validation. – Using the doSNOW package to accelerate caret machine learning training by using multiple CPUs in parallel. – Using caret to train single decision trees on text features and tune the trained model for optimal accuracy. – Evaluating the results of the cross validation process. 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 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 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JNF0 See what our past attendees are saying here: https://hubs.ly/H0f5K120 -- 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: 16081 Data Science Dojo
SAS Visual Text Analytics Demo
 
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http://www.sas.com/vta Mary Beth Ainsworth, SAS Global Product Marketing Manager for Text Analytics, and Simran Bagga, Principal Product Manager for Text Analytics at SAS, provide a look at SAS Visual Text Analytics in action. LEARN MORE ABOUT SAS VISUAL TEXT ANALYTICS Get maximum value from your unstructured data using a wide variety of modeling approaches – including supervised and unsupervised machine learning, linguistic rules, categorization, entity extraction, sentiment analysis and topic detection. SAS Visual Text Analytics helps you overcome the challenges of identifying and categorizing large volumes of text data. http://www.sas.com/vta SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 4925 SAS Software
SAS Tech Talk: Analyze Medical Reports with SAS Text Analytics
 
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Greg Massey, Senior Analytical Consultant, shows SAS Text Analytics applied to medical pathology reports. Recorded live at SAS Global Forum 2014. To learn more about SAS Text Analytics and Content Categorization, visit http://www.sas.com/en_us/software/analytics/enterprise-content-categorization.html.
Views: 840 SAS Software