In this video I process transcriptions from Hugo Chavez's TV programme "Alo Presidente" to find patterns in his speech. Watching this video you will learn how to: -Download several documents at once from a webpage using a Firefox plugin. - Batch convert pdf files to text using a very simple script and a java application. - Process documents with Rapid Miner using their association rules feature to find patterns in them.
Views: 35738 Alba Madriz
In this Rapidminer Video Tutorial I show the user how to use the web crawling and text mining operators to download 4 web pages, build a word frequency list, and then check out the similarities between the web sites. Hat tip to Neil at Vancouver.blogspot.com and the Rapid-I team.
Views: 21833 NeuralMarketTrends
Visit http://julialang.org/ to download Julia.
Views: 1425 The Julia Language
This tutorial shows how to conduct text sentiment analysis in R. We'll be pulling tweets from the Twitter web API, comparing each word to positive and negative word bank, and then using a basic algorithm to determine the overall sentiment. We'll then create several charts and graphs to organize the data. Updated code: http://silviaplanella.wordpress.com/2014/12/31/sentiment-analysis-twitter-and-r/ https://github.com/mjhea0/twitter-sentiment-analysis https://gist.github.com/mjhea0/5497065 TwitteR docs - http://cran.r-project.org/web/packages/twitteR/twitteR.pdf
Views: 64566 Michael Herman
So for a quick description of PDF structure please go to my blog: sketchymoose.blogspot.com This shows the analysis of a PDF document grabbed from Contagio. All tools used can be found in my blog posting. This is by no means exhaustive (far from it) analysis of a PDF. Just wanted to show people a quick and dirty analysis. I also use additional programs on here to observe the malicious file that is dropped and what it tries to do.
Views: 5650 TheSketchymoose
http://togotv.dbcls.jp/20110307.html#p01 In this video, Goran Nenadic who is a Senior Lecturer (Associate Professor) in the School of Computer Science, University of Manchester and a group leader in the Manchester Interdisciplinary BioCenter talks about text mining from biomedical literature. The talk has been at Workshop on Parallel and Distributed Processing of Large Genome Data organized by GCOE Program: Deciphering Genome Sphere from Genome Big Bang.
Views: 1538 togotv
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: 3824 KNIMETV
This tutorial starts with introduction of Dataset. All aspects of dataset are discussed. Then basic working of RapidMiner is discussed. Once the viewer is acquainted with the knowledge of dataset and basic working of RapidMiner, following operations are performed on the dataset. K-NN Classification Naïve Bayes Classification Decision Tree Association Rules
Views: 30702 RapidMinerTutorial
Follow me on Twitter @amunategui Check out my new book "Monetizing Machine Learning": https://amzn.to/2CRUO The stringdist package in R can help make sense of large, text-based factor variables by clustering them into supersets. This approach preserves some of the content's substance without having to resort to full-on, natural language processing. Code and walkthrough: http://amunategui.github.io/stringdist/ Follow me on Twitter https://twitter.com/amunategui and signup to my newsletter: http://www.viralml.com/signup.html More on http://www.ViralML.com and https://amunategui.github.io Thanks!
Views: 5731 Manuel Amunategui
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: 77387 Linguamatics
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: 66979 deltaDNA
A screencast that teaches you how to install a powerful social web mining toolbox in less than 5 minutes. (Uploaded 28 July 2013) Suggestions for improvement (or any comments at all) are very much appreciated. If this screencast helps you, please leave positive feedback.
Views: 2903 Matthew Russell
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
Views: 1254 Emanuel Vela
A tutorial showing how to import data into RapidMiner. RapidMiner is an open source system for data mining, predictive analytics, machine learning, and artificial intelligence applications. For more information: http://rapid-i.com/ Brought to you by Rapid Progress Marketing and Modeling, LLC (RPM Squared) http://www.RPMSquared.com/
Views: 17182 Predictive Analytics
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.
Views: 238 ChemAxon
This is a example in GATE which shows the results of the default ANNIE pipeline on an English document. In this case the document is "That's what she said" that lovely catch phrase from Michael Scott in The Office TV show http://www.cs.washington.edu/homes/brun/pubs/pubs/Kiddon11.pdf it discusses humor recognition...
Views: 30287 cesine0
This example takes a Course syllabus (mostly semantics courses) and highlights the reading lists using Jape grammars. It recognizes things like Van Fintel and Heim 2003 as a citation and Chapters 1, 3 and 8 as a reading selections and Week 1 as a due date (among others). Its another example of what GATE can do, in this case to help automate tasks like downloading a reading list. The files are in here https://github.com/cesine/GATEinSpring/tree/master/gate/WEB-INF/gate-files
Views: 12706 cesine0
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 2: Exploring the Explorer http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 92359 WekaMOOC
In this first part of video tutorials on WebHarvy we will see how WebHarvy can be easily used to extract data from websites using a point and click interface. https://www.webharvy.com/index.html Download Free Trial :- https://www.webharvy.com/download.html Have questions ? :- https://www.webharvy.com/contact.html
Views: 3388 sysnucleus
Video ini dibuat sebagai tugas akhir dari mata kuliah Pengantar Data Mining. Credit by: H13115002 - WINDI REZA PRATIWI H13115312 - AGREANI M. TANGKELANGI H13115313 - UMMAERAH SAFRIATY J. JIBRIL H13115507 - AMALIYAH AFIFAH AMIR H13115510 - ANDI NIRWANA
Views: 838 Ummaerah Safriaty
Chris McNaboe knows his Syrian opposition armed groups. For the current conflict, he can tell you exactly when a particular brigade formed from previously separate battalions around Aleppo, Syria; how many people are in the brigade; their reason for forming; and what weapons they have. The primary source for this top-level insider info? Facebook, Twitter, and YouTube. Watch the video to learn more about the Carter Center's Syria Conflict Mapping Project. Founded in 1982 by former U.S. President Jimmy Carter and former First Lady Rosalynn Carter in partnership with Emory University, The Carter Center is committed to advancing human rights and alleviating unnecessary human suffering. The Center wages peace, fights disease, and builds hope worldwide.
Views: 649 The Carter Center
Matthew Russell, author of Mining the Social Web, presents an infographic that presents the primary data sources and technologies as introduced in the book. Like mining the social web and download a high resolution image of the graphic shown in the video at http://on.fb.me/icFoXH
Views: 4389 Matthew Russell
This is a tutorial on using QDA Miner to analyze qualitative research. 0:09 - Creating a project 1:23 - Adding a code 2:23 - Coding a segment of text 4:14 - Highlight or dim already-coded text 4:57 - Text retrieval - list all instances of a keyword 7:16 - Coding retrieval - list all instances of a code 9:30 - Coding frequency - count how many times each code appears QDA Miner runs on Windows. Download: http://www.provalisresearch.com/Downl... And there are several workarounds to run it on a Mac: http://provalisresearch.com/products/... An alternative program, which runs on both Mac and Windows, is Qualyzer: http://qualyzer.bitbucket.org/downloa... http://qualyzer.bitbucket.org/getStar...
Views: 35962 Sam Long
See how PDF2Data extracts data from documents. PDF2Data can be used for invoice processing, data extraction, text extraction etc... Please, see video in HD quality. http://www.cloudforpeople.com
Views: 1898 cloud4people
Our genome is an amazing sequence of three billion chemical letters (DNA nucleotides) that is present in almost every cell inside the human body. This sequence contains fragments called genes that encode proteins with a wide diversity of functions. Any mutation in the gene sequence might result in an alteration of these functions, which sometimes is undesirable and contributes to disease. Hence identifying which genes are associated with which disease is of great medical importance. It is a key step to diagnosing and curing diseases, and hence plays a key role in many critical applications such as personalized medicine and early prediction, and drug design and repurposing. However this task is not trivial, especially with the exponential growth of genomic data that makes it challenging for the geneticists to explore all possible hypotheses in a reasonable amount of time. In this thesis, we propose Beegle, an online search and discovery engine, which allows geneticists to explore possible hypotheses about links between genes and diseases in a fast and easy way. It starts from text mining to quickly present the user with an ordered list of genes that have been reported in the literature to be linked with the query in question. Then it integrates genomic data fusion techniques to learn a model and generate novel gene hypothesis. In this work, we analysed over 20 million biomedical abstracts to extract relevant links between genes and diseases. We tested different statistical measures to decide on the degree of relevance of such links, which ranged from co-occurrence to cosine similarities. We experimented with two biomedical text taggers, which are quite diverse in tagging the biomedical text with the different biomedical concepts. We also investigated the application of topic modelling, where we relied on a latent Dirichlet allocation model, to infer a latent set topics that better model our text data. Finally, we integrated state-ofthe-art learning methodologies to analyse and fuse over 70 genomic data sources and compute gene similarity scores to eventually present the user with one final ranked hypothesis. We release Beegle at http://beegle.esat.kuleuven.be/, where we welcome our users to start their disease-gene discovery experience with an introductory video tutorial. We validated Beegle in multiple experimental setups, which we partly created in-house based on public genetic databases. We mainly designed the validation process such that it mimics real discovery, where we limited information in our data sets up to a certain date, then we used test sets of disease-gene links that were only reported after this date. Hence, our hypotheses were not contaminated with novel information. In one setup, our results show that Beegle recommends on average 41.2% true novel hypotheses in the top 5% ranking genes. In another setup, our results show that Beegle recommends at least one true novel hypothesis in the top 20 ranking genes. Our methodology increases the true positive rate of manual approaches by 44%, and reduces the error of automatic approaches by 50%. We believe Beegle is an interesting tool to quickly explore all the gene hypotheses related to any query of interest. These can further be assessed and filtered by the geneticist who can carry out the necessary validation experiments. This motivates us to extend Beegle such that it additionally explores similar drug hypotheses, which we believe is a potential future work given the availability of the relevant data sets.
Views: 93 sarahelshal
A quick look at the new Rapidminer 5.0. In this video we check out how the GUI changed and how to load in an Excel spreadsheet and run a simple neural net through it. Please vote and comment! I have a fragile ego! LOL.
Views: 114510 NeuralMarketTrends
An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 298921 Data School
To download, please go to http://www.sobolsoft.com/extractparagraphsentence/
Views: 989 Peter Sobol
Hi Friends,We may come across the situation when we need to extract text from images, or we may need to convert some scanned copy into text. Converting the scanned copy to text is done using Optical Character Recognition Software which is not free most of time. You need to spend some good amount of money to get them and convert images to text.However paying the hefty price just for a single usage does not seems to be a good idea. In this video, I will show you how to extract text from Image for free using Google Drive! Google Drive provides you the OCR technology and we make use of it to convert images to text. Let us see steps which we need to follow to convert images to text or to convert PDF files to text. On your computer, go to drive dot google dot com.Login with your google account. Click on the New button and select File Upload to upload images file which you want to convert to text. Select the particular image file and it gets uploaded to Google Drive. Once the files gets uploaded, right click on the image file and move towards Open with Google Docs. Now, new tab opens with the image surrounded by the blue border and the corresponding editable text at the bottom. You can resize the blue border, based on the content which you want. Once you are sure about the required content, remove the image from the tab, save the remaining text and close the tab.Once converted, you have an option to edit it in Google Drive, or download it in your preferred format and edit in your computer with your favourite text editor. Please note that the accuracy of the text transcribed may vary depending on the quality of the image being read from the words in it. Clear images with high contrast are likely to give best results. Also, do note that you can only upload a file sized 2MB and below, and only the first ten pages of a PDF file will get converted. If you have a PDF file with tons of pages, do split it into several files before uploading. I hope you liked the information shared on this video. For more such videos,you can subscribe to my channel Tech Curious. Thanks for watching
Views: 311 TechCurious
To download this software, please go to http://www.sobolsoft.com/extractdata/
Views: 1273 Peter Sobol