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Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
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Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 42378 Well Academy
What is Text Mining?
 
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An introduction to the basics of text and data mining. To learn more about text mining, view the video "How does Text Mining Work?" here: https://youtu.be/xxqrIZyKKuk
Views: 43834 Elsevier
R tutorial: What is text mining?
 
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Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches. Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features. This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon. The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output. The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically. Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output. Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text. In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes. Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses! Let’s get a quick taste of text mining!
Views: 21264 DataCamp
INTRODUCTION TO TEXT MINING IN HINDI
 
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find relevant notes at-https://viden.io/
Views: 7023 LearnEveryone
What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning, definition & explanation
 
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What is TEXT MINING? What does TEXT MINING mean? TEXT MINING meaning - TEXT MINING definition - TEXT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities). Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation. The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining" in 2004 to describe "text analytics." The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s, notably life-sciences research and government intelligence. The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80 percent of business-relevant information originates in unstructured form, primarily text. These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing.
Views: 1807 The Audiopedia
Web Mining - Tutorial
 
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Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
What is the difference between keyword search and text mining?
 
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This is a brief introduction to the difference between keyword search and text mining. Find out more from the leader in natural language based text mining solutions, by visiting our blog and website: http://www.linguamatics.com/blog Following us on social media: Twitter: www.twitter.com/linguamatics LinkedIn: www.linkedin.com/company/linguamatics Facebook: www.facebook.com/Linguamatics YouTube: https://www.youtube.com/user/Linguama... You can contact us with questions at enquiries @ linguamatics.com
Views: 516 Linguamatics
Brian Carter: Lifecycle of Web Text Mining: Scrape to Sense
 
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Pillreports.net is an on-line database of reviews of Ecstasy pills. In consumer theory illicit drugs are experience goods, in that the contents are not known until the time of consumption. Websites like Pillreports.net, may be viewed as an attempt to bridge that gap, as well as highlighting instances, where a particular pill is producing undesirable effects. This talk will present the experiences and insights from a text mining project using data scraped from the Pillreports.net site.The setting up and the benefits, ease of using BeautifulSoup package and pymnogo to store the data in MongoDB will be outlined.A brief overview of some interesting parts of data cleansing will be detailed.Insights and understanding of the data gained from applying classification and clustering techniques will be outlined. In particular visualizations of decision boundaries in classification using "most important variables". Similarly visualizations of PCA projections for understanding cluster separation will be detailed to illustrate cluster separation. The talk will be presented in the iPython notebook and all relevant datasets and code will be supplied. Python Packages Used: (bs4, matplotlib, nltk, numpy, pandas, re, seaborn, sklearn, scipy, urllib2) Brian Carter
Views: 1169 PyData
How does Text Mining Work?
 
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Understand the basics of how text and data mining works and how it is used to help advance science and medicine. To learn what text mining is, view the video "What is Text Mining?" here: https://youtu.be/I3cjbB38Z4A
Views: 11922 Elsevier
Topic Detection with Text Mining
 
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Meet the authors of the e-book “From Words To Wisdom”, right here in this webinar on Tuesday May 15, 2018 at 6pm CEST. Displaying words on a scatter plot and analyzing how they relate is just one of the many analytics tasks you can cover with text processing and text mining in KNIME Analytics Platform. We’ve prepared a small taste of what text mining can do for you. Step by step, we’ll build a workflow for topic detection, including text reading, text cleaning, stemming, and visualization, till topic detection. We’ll also cover other useful things you can do with text mining in KNIME. For example, did you know that you can access PDF files or even EPUB Kindle files? Or remove stop words from a dictionary list? That you can stem words in a variety of languages? Or build a word cloud of your preferred politician’s talk? Did you know that you can use Latent Dirichlet Allocation for automatic topic detection? Join us to find out more! Material for this webinar has been extracted from the e-book “From Words to Wisdom” by Vincenzo Tursi and Rosaria Silipo: https://www.knime.com/knimepress/from-words-to-wisdom At the end of the webinar, the authors will be available for a Q&A session. Please submit your questions in advance to: [email protected] This webinar only requires basic knowledge of KNIME Analytics Platform which you can get in chapter one of the KNIME E-Learning Course: https://www.knime.com/knime-introductory-course
Views: 2159 KNIMETV
Text mining: Key concepts and applications
 
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Jee-Hyub Kim and Senay Kafkas from the Literature Services team at EMBL-EBI present this talk on an introduction to text mining and its applications in service provision. The 1st part of this talk focuses on what text mining is and some of the methods and available tools. The 2nd part looks at how to find articles on Europe PMC - a free literature resource for biomedical and health researchers - and how to build your own text mining pipeline (starts at 20:30 mins). The final part gives a nice case study showing how Europe PMC's pipeline was integrated into a new drug target validation platform called Open Targets (previously CTTV) (starts at 38:20 mins). This video is best viewed in full screen mode using Google Chrome.
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1461 Kakoli Bandyopadhyay
Text mining with correspondence analysis
 
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Here is an example of the use of correspondence analysis for textual data. Four methods of multivariate data analysis are descibed by words and compared with correspondence analysis.
Views: 4413 François Husson
Text Mining (part 3)  -  Sentiment Analysis and Wordcloud in R (single document)
 
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Sentiment Analysis Implementation and Wordcloud. Find the terms here: http://ptrckprry.com/course/ssd/data/positive-words.txt http://ptrckprry.com/course/ssd/data/negative-words.txt
Views: 19396 Jalayer Academy
Text Mining for Beginners
 
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This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 73867 Linguamatics
What is Text Mining?
 
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The introduction of Text Mining-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 2906 Jian Cui
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: 392492 sentdex
#FixCopyright:  Copyright & Research - Text & Data Mining (TDM) Explained
 
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Read our blog post analysing the European Commission's (EC) text and data mining (TDM) exception and providing recommendations on how to improve it: http://bit.ly/2cE60sp Copy (short for Copyright) explains what text and data mining (TDM) is all about, and what hurdles researchers are currently facing. We also have a blog post on the TDM bits in the EC's Impact Assessment accompanying the proposal: http://bit.ly/2du9sYe Read more about the EC's copyright reform proposals in general: http://bit.ly/2cvAh0a
Views: 3085 FixCopyright
R - Twitter Mining with R (part 1)
 
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Twitter Mining with R part 1 takes you through setting up a connection with Twitter. This requires a couple packages you will need to install, and creating a Twitter application, which needs to be authorized in R before you can access tweets. We quickly go through this entire process which may take some flexibility on your part so be patient and be ready troubleshoot as details change with updates. Warning: You are going to face challenges setting up the twitter API connection. The steps for this part have been known to change slightly over time for a variety of reasons. Follow the general steps and expect a few errors along the way which you will have to troubleshoot. It is hard to solve these issues remotely from where I am.
Views: 62938 Jalayer Academy
Web Crawler - CS101 - Udacity
 
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Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/f16/ Sergey Brin, co-founder of Google, introduces the class. What is a web-crawler and why do you need one? All units in this course below: Unit 1: http://www.youtube.com/playlist?list=PLF6D042E98ED5C691 Unit 2: http://www.youtube.com/playlist?list=PL6A1005157875332F Unit 3: http://www.youtube.com/playlist?list=PL62AE4EA617CF97D7 Unit 4: http://www.youtube.com/playlist?list=PL886F98D98288A232& Unit 5: http://www.youtube.com/playlist?list=PLBA8DEB5640ECBBDD Unit 6: http://www.youtube.com/playlist?list=PL6B5C5EC17F3404D6 Unit 7: http://www.youtube.com/playlist?list=PL6511E7098EC577BE OfficeHours 1: http://www.youtube.com/playlist?list=PLDA5F9F71AFF4B69E Join the class at http://www.udacity.com to gain access to interactive quizzes, homework, programming assignments and a helpful community.
Views: 116238 Udacity
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: 14740 Linguamatics
Building Smarter Web Applications with Text Mining
 
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Bashar Al-Fallouji http://2014.osdc.com.au/presentation/19-building-smarter-web-applications-text-mining http://2014.osdc.com.au/presentation/19-building-smarter-web-applications-text-mining
Views: 95 OSDC Australia
"Data Science" What Is Text Mining ? | Applications Of Text Mining And Clustering | Training -ExcelR
 
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What Is Text Mining ? | Applications Of Text Mining And Clustering #DataScience #TextMining #Clustering #TextMining In Datascience #Applications of Textmining #training #2018 Text Mining, Hmm before we dive into the water we need to make sure that we are ready. So in the same way before going into deep we should know what is text-mining and it`s applications? Daily a new data is being generated and mostly majority of data is unstructured. To convert/transform the unstructured data (unstructured text data) into structured, then the unstructured text data is to set for analysis using text-mining to get new generated information. Like we get daily 1. Call transcripts, 2. Emails that we sent to customer service 3. Social Media outreach (Facebook, twitter, Instagram and many more) 4. Speech transcripts 5. Filed agents, sales people 6. Interviews and survey`s The process of getting high quality of information deriving from the text data is text –mining. To examine the large amount of text data/Written data sources to generate new information. This quality information I typically derived through devising of patters and trends such as statistical pattern learning. Clustering in data mining is gathering set of abstract data and aggregating them based on their similarities. Here are some of the applications of text mining and clustering are: 1. Text categorization into particular domains 2. Organizing a set of documents automatically by text Clustering. 3. Identifying and extracting subject information in documents. In other words-sentiment analysis. 4. Extracting entity/concepts which can identify people, places, organisations and other entities. 5. Learning relations between named entities. In this video you will learn about 1. Text Mining and use of Clustering 2. Applications of Text Mining 3. What is Word Cloud? SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/excelr-solutions/ Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 131400 Brandon Weinberg
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: 25594 OdinText
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: 210 Statgraphics
SAS TextMining Introduction
 
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Introduction to Text Mining
Views: 1153 Dothang Truong
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #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 Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Website: https://www.edureka.co/data-science 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. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 49513 edureka!
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
 
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Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. ------- Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 1535 Quantopian
ODSC West 2015 | Ted Kwartler "Introduction to text mining using R"
 
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Abstract: Attendees will learn the foundations of text mining approaches in addition to learn basic text mining scripting functions used in R. The audience will learn what text mining is, then perform primary text mining such as keyword scanning, dendogram and word cloud creation. Later participants will be able to do more sophisticated analysis including polarity, topic modeling and named entity recognition. Bio: Ted Kwartler is the Director of Customer Success at DataRobot where he manages the end-to-end customer journey. He advocates for and integrates customer innovation into everyday culture and work. He helps to define and organize all customer service functions and key performance indicators. Thus, he incorporates data-driven customer analytics decisions balanced with qualitative feedback to continuously innovate for the customer experience. Specialties: Statistical forecasting and data mining, IT service management, customer service process improvement and project management, business analytics.
Views: 1427 Open Data Science
Linking Text Mining Efforts to Semantic Web
 
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http://togotv.dbcls.jp/20130629.html http://www.slideshare.net/jindong/pubannotation-ontocloudlodqa NBDC / DBCLS BioHackathon 2013 was held in Tokyo, Japan. Main focus of this BioHackathon is semantic interoperability and standardization of bioinformatics data and Web services. The participants discussed, explored and developed web applications and interoperability (DDBJ/UniProt, SADI, TogoGenome, Schema.org etc.), generation and standardization of RDF data (Open Bio* tools, SIO, FALDO, Identifiers.org etc.), text-mining, NLP and ontology mapping (LODQA, BioPortal, NanoPublication etc.), quality assessment of SPARQL endpoints (availability, contents, CORS etc.) and standardization of RDF data in specific domains. On the first day of the BioHackathon (Jun. 23), public symposium of the BioHackathon 2013 was held at Tokyo Skytree Space 634. In this talk, Jin-Dong Kim makes a presentation entitled "Linking Text Mining Efforts to Semantic Web".
Views: 518 togotv
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 230947 Siraj Raval
Twitter text mining with R
 
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You may visit my website for video and R codes. http://web.ics.purdue.edu/~jinsuh/analyticspractice-twitter.php
Views: 989 Jinsuh Lee
Edictalis - Analyse de texte (text mining), contenu marketing, SEO.
 
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Edictalis.com est une société spécialisée dans l'analyse de texte (fouille de texte - text mining), la rédaction (contenu web, marketing, rapports...), le référencement naturel (Seo), la correction ainsi que la transcription. Le texte est notre cœur de métier depuis 2005. Plus de détails sur http://www.edictalis.com
Text Mining (part4)  -  Postive and Negative Terms for Sentiment Analysis in R
 
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Find the terms here: http://ptrckprry.com/course/ssd/data/positive-words.txt http://ptrckprry.com/course/ssd/data/negative-words.txt
Views: 8869 Jalayer Academy
What Is The Meaning Of Text Mining?
 
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Text analytics software can help by transposing words and phrases in unstructured data into numerical values which text mining, also referred to as roughly equivalent analytics, is the process of deriving high quality information from. 57 data and text mining has been defined as automated analytical techniques that work by 'copying existing electronic information, for instance articles in meaningcloud market leading solutions for text mining and voice of the way to extract the meaning of all kind of unstructured content social conversation, What is text mining (text analytics)? Definition from whatis about text mining ibm. Text mining defined questions answered logi analyticstext for beginners what is text mining? Stony brook cs. Text mining wikipedia what is text (text analytics)? Definition from whatis searchbusinessanalytics. Googleusercontent search. Marti hearst what is text mining? . Text mining? What does text mining mean? mining, big data, unstructured data statistica. Text analytics meaningcloud text mining solutions. High quality information is typically derived through the devising of patterns and trends means such as statistical pattern learning text mining process analyzing collections textual materials in order to linguistics based finds meaning much people do by apr 23, 2013 mining, which sometimes referred analytics one way make qualitative or unstructured data usable a computer. What is text data mining? Definition from techopedia. Prelude overview introduction to text mining tutorialwhat does previously unknown mean? Implies discovering genuinely new apr 29, 2016 even if the definition of may look simple, operations, are not. What is text mining (text analytics)? Definition from whatis about ibm. Text mining definition of text by the free dictionary. Text mining attempts to derive meaning from the words and sentences in therefore, text analytics software has been created that uses natural language processing algorithms find huge amounts of 11. How? Determine patterns and trends within text through statistical pattern learning a simple explanation of how mining works, it differs from keyword search can understand real meanings thanks to sophisticated natural language witte, r. Learn more on how text mining works oct 17, 2003 is the discovery by computer of new, previously even though meaning texts are not being discerned programs ''jan 18, 2017. What is text mining? Information space. That's sick! text mining and words with multiple definitions definition from pc magazine encyclopediawhat is analytics? Clarabridgealrc. Qualitative data is descriptive that cannot be measured in numbers and often includes qualities of appearance like color, texture, textual description text mining means deriving insights from. Text mining wikipedia. The application of text mining techniques to solve business problems is called analytics. There are text mining applications which offer 'black box' methods to extract 'deep meaning' data definition
What is an Ontology
 
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Description of an ontology and its benefits. Please contact [email protected] for more information.
Views: 137517 SpryKnowledge
What is Web Mining
 
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Views: 12614 TechGig
Intro to Web Scraping with Python and Beautiful Soup
 
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Web scraping is a very powerful tool to learn for any data professional. With web scraping the entire internet becomes your database. In this tutorial we show you how to parse a web page into a data file (csv) using a Python package called BeautifulSoup. In this example, we web scrape graphics cards from NewEgg.com. Sublime: https://www.sublimetext.com/3 Anaconda: https://www.continuum.io/downloads#wi... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3500+ employees from over 700 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f6wzS0 See what our past attendees are saying here: https://hubs.ly/H0f6wzY0 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 403537 Data Science Dojo
R PROGRAMMING TEXT MINING TUTORIAL
 
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Learn how to perform text analysis with R Programming through this amazing tutorial! Podcast transcript available here - https://www.superdatascience.com/sds-086-computer-vision/ Natural languages (English, Hindi, Mandarin etc.) are different from programming languages. The semantic or the meaning of a statement depends on the context, tone and a lot of other factors. Unlike programming languages, natural languages are ambiguous. Text mining deals with helping computers understand the “meaning” of the text. Some of the common text mining applications include sentiment analysis e.g if a Tweet about a movie says something positive or not, text classification e.g classifying the mails you get as spam or ham etc. In this tutorial, we’ll learn about text mining and use some R libraries to implement some common text mining techniques. We’ll learn how to do sentiment analysis, how to build word clouds, and how to process your text so that you can do meaningful analysis with it.
Views: 2036 SuperDataScience
Use Tableau to Analyze Twitter Data - #HAPPY
 
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Tutorial on using Tableau's web data connector to connect to Twitter. I am using Tableau version 10 for this walkthrough. 1. We go to connect, select More on Server, and select Web Data Connector 2. Enter the URL of the Twitter Web Data Connector and your desired search term – If you would like to build your own Twitter web data connector you can use instructions listed on TableauJunkie’s post – http://tableaujunkie.com/post/119681578798/creating-a-twitter-web-data-connector 3. Now let’s search for the word #happy just as an example 4. Hit “Update Now” 5. We can see that we were able to get several data columns into our view – we have a. The open text – or status text b. # of times it was retweeted c. User names, locations, d. And the date that it was tweeted 6. Alright, now let’s go ahead and take a look at the data a. Let’s place user status count, and time zone country into the view and create a map view to see where @happy was tweeted b. Let’s put time zone country on label and create a filled map c. It looks like the United States is the “happiest country” according to our simple analysis d. Now let’s create a continuous area chart that will show the number of tweets as of created time – with the level of detail on minutes this is showing us how many tweets used #happy in the past few minutes e. We can turn on the labels and user time zone country add to color
Views: 5015 Story by Data
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: 12714 Lutz Finger
R tutorial: Getting started with text mining?
 
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Learn more about text mining with R: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Boom, we’re back! You used bag of words text mining to make the frequent words plot. You can tell you used bag of words and not semantic parsing because you didn’t make a plot with only proper nouns. The function didn’t care about word type. In this section we are going to build our first corpus from 1000 tweets mentioning coffee. A corpus is a collection of documents. In this case, you use read.csv to bring in the file and create coffee_tweets from the text column. coffee_tweets isn’t a corpus yet though. You have to specify it as your text source so the tm package can then change its class to corpus. There are many ways to specify the source or sources for your corpora. In this next section, you will build a corpus from both a vector and a data frame because they are both pretty common.
Views: 4350 DataCamp
Text mining using rapidminer tutorial
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-systems-cybernetics/
Views: 357 PHD Projects
Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon University
 
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Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon University Today, more data is accumulated than ever before. It has been estimated that over 80% of data collected by businesses is unstructured, mostly in the form of free text. The statistical community has developed many tools for analyzing textual data, both in the areas of exploratory data analysis (e.g. clustering methods) and predictive analytics. In this talk, Philipp Burckhardt will discuss tools and libraries that you can use today to perform text mining with Node.js. Creative strategies to overcome the limitations of the V8 engine in the areas of high-performance and memory-intensive computing will be discussed. You will be introduced to how you can use Node.js streams to analyze text in real-time, how to leverage native add-ons for performance-intensive code and how to build command-line interfaces to process text directly from the terminal.
Views: 2317 node.js
Text Mining Tutorials for Beginners | Importance of Text Mining | Data Science Certification -ExcelR
 
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ExcelR: Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Things you will learn in this video 1) What is Text mining? 2) How clustering techniques helps and text data analysis? 3) What is word cloud? 4) Examples for text mining 5) Text mining terminology and pre-processing To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here " https://goo.gl/JTkWXo" SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For K-Means Clustering Tutorial click here https://goo.gl/PYqXRJ For Introduction to Clustering click here Introduction to Clustering | Cluster Analysis #ExcelRSolutions #Textmining #Whatistextmining #Textminingimportance #Wordcloud #DataSciencetutorial #DataScienceforbeginners #DataScienceTraining ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
Text Mining with Big Data
 
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The video illustrates how text mining techniques allow the analysis of text written in natural language, in order to detect semantic relationships and enable text classification. Audio in Italian. English subtitles available. Illustrations developed by Monica Franceschini, Solution Architecture Manager, Big Data & Analytics Competency Center, Engineering Group.
Views: 300 ItalyMadeOpenSource

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