Code : https://goo.gl/xUjhg2 Python Core ------------ Video in English https://goo.gl/df7GXL Video in Tamil https://goo.gl/LT4zEw Python Web application ---------------------- Videos in Tamil https://goo.gl/rRjs59 Videos in English https://goo.gl/spkvfv Python NLP ----------- Videos in Tamil https://goo.gl/LL4ija Videos in English https://goo.gl/TsMVfT Artificial intelligence and ML ------------------------------ Videos in Tamil https://goo.gl/VNcxUW Videos in English https://goo.gl/EiUB4P ChatBot -------- Videos in Tamil https://goo.gl/JU2WPk Videos in English https://goo.gl/KUZ7PY YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English

Learn how to read and extract PDF data. Whether in native text format or scanned images, UiPath allows you to navigate, identify and use PDF data however you need. Read PDF. Read PDF with OCR.

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal

This a basic program for understanding PyPDF2 module and its methods. Simple program to read data in a PDF file.

A very basic example: convert unstructured data from text files to structured analyzable format.

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

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...).

Download the PDF to keep as reference http://theexcelclub.com/extract-key-phrases-from-text/ FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Or on Android App https://play.google.com/store/apps/details?id=com.PBI.trainigapp Carry out a text analytics like the big brand...only for free with Power BI and Microsoft Cognitive Services. this video will cover Obtain a Text Analytics API Key from Microsoft Cognitive Services Power BI – Setting up the Text Data Setting up the Parameter in Power BI Setting up the Custom function Query(with code to copy) Grouping the text Running the Key Phrase Extraction by calling the custom function. Extracting the key phrases from the returned Json file. Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y Join the online Excel and PowerBI community https://plus.google.com/u/0/communities/110804786414261269900

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.

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

For additional information on PDFtools click on the following links: https://cran.r-project.org/web/packages/pdftools/pdftools.pdf https://github.com/ropensci/pdftools

This webcast shows some of the features of the Qiqqa PDF viewer.

Example of Text Extraction feature. It lets see the keywords extracted from the content of a document.

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

** 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 sales[email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).

Demonstrates extracting text contents from PDF by hand, using basic UNIX tools only. PDFMiner (PDF extraction tool in Python): http://www.unixuser.org/~euske/python/pdfminer/

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

Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip

Text Mining with R. Import a single document into R.

One of the most common challenges in business today is extracting data from formatted reports so that the underlying data can be analyzed in a flexible way. The default solution to this problem is re-keying printed reports into spreadsheets. That is a very time-consuming and error-prone method, especially if it has to be repeated on a monthly, weekly or even daily basis. Let’s take a look at a better way… Datawatch makes the data acquisition process simple and easy through a drag-and-drop interface that intelligently parses PDF reports and other desktop files, and extracts the data it finds into a flat table of rows and columns. Occasionally the automatic parser needs some human guidance to ensure it is interpreting the report data correctly. These fine-tuning operations are also presented in an intuitive way. This table can then be sent to downstream applications and business processes, or further prepared and joined with other data to get a complete view of the information. But it doesn’t end here. With Datawatch, to ACQUIRE data means reaching and loading data where ever it is, in whatever format it is. In addition to loading semi-structured and multi-structured data, Datawatch offers out-of-the-box connectivity to a large number of structured data sources. Your data can be stored locally or online, in a file or in a database, it can be historic data-at-rest or streaming data generated in the moment – Datawatch lets you use it all.

In this video I demonstrate a program I wrote that can read PDF invoices and turn them into journal entries for an accounting system (through a csv file). A similar program could be used to extract other kinds of data and create an equally useful csv file.

Import multiple text documents and create a Corpus.

Text Analytics Toolbox™ provides tools for extracting text from documents, preprocessing raw text, visualizing text, and performing machine learning on text data. The typical workflow begins by importing text data from documents, such as PDF and Microsoft® Word® files, and then extracting meaningful words from the data. Once text is preprocessed, you can interact with your data in a number of ways, including converting the text into a numeric representation and visualizing the text with word clouds or scatter plots. Features created with Text Analytics Toolbox can also be combined with features from other data sources to build machine learning models that take advantage of textual, numeric, audio, and other types of data. You can import pretrained word-embedding models, such as those available in word2vec, FastText, and GloVe formats, to map the words in your dataset to their corresponding word vectors. You can also perform topic modeling and dimensionality reduction with machine learning algorithms such as LDA and LSA. To get started transforming large sets of text data into meaningful insight, download a free trial of Text Analytics Toolbox: http://bit.ly/2Jp3t6a Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.

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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.

Learn how to examine a column of unstructured text using IBM SPSS Modeler with Text Analytics

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

References: Text Analytics – The Most Powerful Weapon In Your Arsenal! - http://www.edvancer.in/introduction-text-analytics/ Watson – A System Designed for Answers - http://www-03.ibm.com/innovation/us/engines/assets/9442_Watson_A_System_White_Paper_POW03061-USEN-00_Final_Feb10_11.pdf Parallel Distributed Text Mining in R Stefan Theussl1 - http://statmath.wu.ac.at/~theussl/conferences/abstracts/ifcs_2009-abstract_A.pdf Transform clinical and operational decision making with IBM Content and Predictive Analytics for Healthcare - https://www-01.ibm.com/software/ecm/offers/programs/icpa.html IBM Watson and Medical Records Text Analytics - http://www-01.ibm.com/software/ebusiness/jstart/downloads/MRTAWatsonHIMSS.pdf IBM Watson: How it Works - https://www.youtube.com/watch?v=_Xcmh1LQB9I Open architecture helps Watson understand natural language - https://www.ibm.com/blogs/research/2011/04/open-architecture-helps-watson-understand-natural-language/ Unstructured Information Management Architecture SDK - https://www.ibm.com/developerworks/data/downloads/uima/ Open architecture helps Watson understand natural language - https://www.ibm.com/blogs/research/2011/04/open-architecture-helps-watson-understand-natural-language/ The Impact of Cognitive Computing on Healthcare - http://mihin.org/wp-content/uploads/2015/06/The-Impact-of-Cognitive-Computing-on-Healthcare-Final-Version-for-Handout.pdf Why IBM’s Watson Health buys let us peek behind the curtain to the future of healthcare - http://medcitynews.com/2016/03/watson-health-future-of-healthcare/ Glassdoor – IBM Data Scientist IBM Watson - http://ibmwatson237.weebly.com/advantages--disadvantages.html IBM Watson Engagement Advisor: Advantages and Disadvantages - http://infotechwea.blogspot.com/2013/05/ibm-watson-engagement-advisor.html IBM Watson -- How to replicate Watson hardware and systems design for your own use in your basement -https://www.ibm.com/developerworks/community/blogs/InsideSystemStorage/entry/ibm_watson_how_to_build_your_own_watson_jr_in_your_basement7?lang=en

Recognize text from image using Python+ OpenCv + OCR. Buy me a coffee https://www.paypal.me/tramvm/5 if you think this is a helpful. Source code: http://www.tramvm.com/2017/05/recognize-text-from-image-with-python.html Relative videos: 1. ORM scanner: https://youtu.be/t66OAXI9mkw 2. Recognize answer sheet with mobile phone: https://youtu.be/82FlPaQ92OU 3. Recognize marked grid with USB camera: https://youtu.be/62P0c8YqVDk 4. Recognize answers sheet with mobile phone: https://youtu.be/xVLC4WdXvhE

Dr. Nees Jan van Eck gives an introduction to VOSviewer. VOSviewer is a software tool for constructing and visualizing bibliometric networks. These networks may for instance include journals, researchers, or individual publications, and they can be constructed based on co-citation, bibliographic coupling, or co-authorship relations. VOSviewer also offers text mining functionality that can be used to construct and visualize co-occurrence networks of important terms extracted from a body of scientific literature. More information can be found at: http://www.vosviewer.com

"There’s a proliferation of unstructured data. Companies collect massive amounts of news feed, emails, social media, and other text-based information to get to know their customers better or to comply with regulations. However, most of this data is unused and untouched. Natural language processing (NLP) holds the key to unlocking business value within these huge data sets, by turning free text into data that can be analyzed and acted upon. Join this tech talk and learn how you can get started mining text data effectively and extracting the rich insights it can bring. We will also demonstrate how you can build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service. Learning Objectives: - Get an introduction to Natural Language Processing (NLP) - Learn benefits of new approaches to analytics and technologies that help empower better decisions, e.g., NLP, data prep - Build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service in a step by step demo"

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

This video discusses how to represent words by vectors, as prescribed by word2vec. It features Martin Jaggi, Assistant Professor of the IC School at EPFL. https://people.epfl.ch/martin.jaggi Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean (2013). Efficient Estimation of Word Representations in Vector Space. https://arxiv.org/pdf/1301.3781v3.pdf Omar Levy and Yoav Goldberg (2014). Neural Word Embedding as Implicit Matrix Factorization. https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf

Using the groupby function to compute the percentage of documents associated with positive or negative sentiment in the IMDB movie review data

http://www.documentsnap.com - This video answers two questions that I get all the time: 1) How do you tell if a PDF is searchable, and 2) How do you extract text from a PDF?

This video highlights the navigation capabilities of the PolyVista Interactive PDFs for Text Analysis.

From: http://a-pdf.com/faq/how-to-extract-text-from-specific-pages-in-pdf-file.htm. A-PDF Text Extractor is an independent PDF manage tool for you to extract or grab text from PDF, quickly and easily, without need to use Adobe PDF tool. Learn more: http://a-pdf.com/text/index.htm

Get a handy PDF version of the How to Mine Ethereum on Your PC guide at: http://blog.cryptocurrencygear.com/ethereum-mining-guide-pdf Tired of refreshing your miner page to check that they are still up? This video will walk you through setting up your miner pool settings (using Etheremine.org for this example) and using a free IFTTT automation applet to send Text messages directly to your phone any time a miner goes down! The IFTTT applet that you need to configure is linked here: https://ifttt.com/applets/58148089d-send-an-sms-when-a-new-gmail-is-from-a-specific-email-address More great crypto mining content to come. Let me know of any questions / comments / feedback to make things better. Thanks! You can support my channel by picking up some gear for your favorite crypto (like the hats and shirts I rock) at https://cryptocurrencygear.com Donations are also welcomed and very appreciated: Eth - 0x3cE75Eca5Ffa84748B43C999D3375CF2c2561550 BTC - 15JeyWEoxN26J2arKbaz8Jp1Q3r3jELwve LTC - LVEb8m4TMcagcJ3VX6xkFWnk6JFJ5YySEf Thanks! Big Shout out to Lee at IMineBlocks and Omar at Crypt0snews who inspired me to make videos and give back to the community so please check out their channels as well.

SMS English ( Lesson) - Modern English abbreviations and Shortened text messages Not only non-native, but also native speakers have problems with the SMS text language when it comes to reading those abbreviated SMSs. Quite often, teachers or elders despair when reading their students or Childrens texts. When worse comes to worse, some people simply shorten their texts to SMS even while writing letters and emails. In this video lesson Niharika brings you some common shortened text messages, so that you don't find yourseld lost in the dictionary whenever you recieve one. Some common shortened text messages: Enjoy the lesson. 1. BRB - Be right back 2. BTW - By the way 3. BYOW - Bring your own booze. 4. LOL - Laugh out loud. 5. ROFL - Rolling of the floor laughing. 6. TGIF - Thank God its Friday. 7. HB2U - Happy birthday to you. 8. SOB - Stressed out bad. 9. Gr8 - Great! 10.TTYL - Talk to you later. 11.ZZZ.. _ Sleeping 12.IDK - I dont know. 13.CIAO - Good bye. 14.2NYT - Tonight 15.LMAO- Laughing my ass out. 16.M8 - Mate 17.NE1 - Anyone 18.EOD - End of debate 19.COS/CUZ - Because 20.N2S - Needless to say 21.CUL8R - Call you later. All acronyms are originally internet slangs but now widespread in other forms of computer mediated communication. So be more emphatic and express your bodily reactions while texting with this SMS.

This video is a part of the webinar "What is new in KNIME 2.10" July 2014. It describes the changes introduced in the TextProcessing and in the Network extension:: - Topic Extractor node - Hierarchy Extractor node - Additional Tree Layouts in the Network Viewer node The full webinar video is available at http://youtu.be/jHOUMbKjum8

Download the PDF to keep as reference http://theexcelclub.com/sentiment-analysis-with-power-bi-and-microsoft-cognitive-services/ FREE Power BI course - Power BI - The Ultimate Orientation http://theexcelclub.com/free-excel-training/ Or on Udemy https://www.udemy.com/power-bi-the-ultimate-orientation Or on Android App https://play.google.com/store/apps/details?id=com.PBI.trainigapp Carry out a sentiment analysis like the big brand...only free with Power BI and Microsoft Cognitive Services. this video will cover Obtain a Text Analytics API Key from Microsoft Cognitive Services Power BI – Setting up the Text Data Setting up the Parameter in Power BI Setting up the Custom function Query(with code to copy) Grouping the text Running the sentiment analysis by calling the custom function. Extracting the sentiment from the returned Json file. Sign up to our newsletter http://theexcelclub.com/newsletter/ Watch more Power BI videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiEsQ-68y0tdnaU9hCqjJ5Dh Watch Excel Videos https://www.youtube.com/playlist?list=PLJ35EHVzCuiFFpjWeK7CE3AEXy_IRZp4y Join the online Excel and PowerBI community https://plus.google.com/u/0/communities/110804786414261269900

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)

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As scientific and patent literature expands, we need more efficient ways to find and extract information. Text mining is already being used successfully to analyse sets of documents after they are found by structure search, in a two‐step process. Integrating name‐to‐structure and structure search directly within an interactive text mining system enables structure search to be mixed with linguistic constraints for more precise filtering. This talk will describe work done in partnership between ChemAxon and Linguamatics in the EU funded project, ChiKEL, including improvements made to name‐to‐structure software, how we evaluated this, and the approach taken to integrating name to structure within the text mining platform, I2E.

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This video demonstrates how to upload a pdf file and link to it from a page or pages. The same instructions would apply for any other type of file, for example word files, powerpoint files, etc.

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