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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: 2702 The Audiopedia
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 68261 deltaDNA
Facebook text analysis on R
 
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For more information, please visit http://web.ics.purdue.edu/~jinsuh/.
Views: 12807 Jinsuh Lee
Minimal Semantic Units in Text Analysis
 
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Speaker: Jake Ryland Williams, Drexel University Presented on December 1, 2017, as part of the 2017 TextXD Conference (https://bids.berkeley.edu/events/textxd-conference) at the Berkeley Institute for Data Science (BIDS) (bids.berkeley.edu).
Robert Meyer - Analysing user comments with Doc2Vec and Machine Learning classification
 
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Description I used the Doc2Vec framework to analyze user comments on German online news articles and uncovered some interesting relations among the data. Furthermore, I fed the resulting Doc2Vec document embeddings as inputs to a supervised machine learning classifier. Can we determine for a particular user comment from which news site it originated? Abstract Doc2Vec is a nice neural network framework for text analysis. The machine learning technique computes so called document and word embeddings, i.e. vector representations of documents and words. These representations can be used to uncover semantic relations. For instance, Doc2Vec may learn that the word "King" is similar to "Queen" but less so to "Database". I used the Doc2Vec framework to analyze user comments on German online news articles and uncovered some interesting relations among the data. Furthermore, I fed the resulting Doc2Vec document embeddings as inputs to a supervised machine learning classifier. Accordingly, given a particular comment, can we determine from which news site it originated? Are there patterns among user comments? Can we identify stereotypical comments for different news sites? Besides presenting the results of my experiments, I will give a short introduction to Doc2Vec. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 18713 PyData
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 165503 Siraj Raval
Natural Language Processing (NLP): Automatic generation of questions and answers from Wikipedia
 
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Presentation by Catherine Henry (2017 Clearwater DevCon). When teaching a subject through text it can be beneficial to evaluate the reader’s understanding; however, the creation of relevant questions and answers can be time-consuming and tedious. I will walk through how the implementation of NLP libraries and algorithms can assist in, and potentially remove altogether, the current necessity of an individual manually formulating these tests.
Views: 1426 Clearwater Analytics
What is BIOMEDICAL TEXT MINING? What does BIOMEDICAL TEXT MINING mean?
 
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What is BIOMEDICAL TEXT MINING? What does BIOMEDICAL TEXT MINING mean? BIOMEDICAL TEXT MINING meaning - BIOMEDICAL TEXT MINING definition - BIOMEDICAL TEXT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Biomedical text mining (also known as BioNLP) refers to text mining applied to texts and literature of the biomedical and molecular biology domain. It is a rather recent research field on the edge of natural language processing, bioinformatics, medical informatics and computational linguistics. There is an increasing interest in text mining and information extraction strategies applied to the biomedical and molecular biology literature due to the increasing number of electronically available publications stored in databases such as PubMed. The main developments in this area have been related to the identification of biological entities (named entity recognition), such as protein and gene names as well as chemical compounds and drugs in free text, the association of gene clusters obtained by microarray experiments with the biological context provided by the corresponding literature, automatic extraction of protein interactions and associations of proteins to functional concepts (e.g. gene ontology terms). Even the extraction of kinetic parameters from text or the subcellular location of proteins have been addressed by information extraction and text mining technology. Information extraction and text mining methods have been explored to extract information related to biological processes and diseases.
Views: 112 The Audiopedia
Word2vec with Gensim - Python
 
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This video explains word2vec concepts and also helps implement it in gensim library of python. Word2vec extracts features from text and assigns vector notations for each word. The word relations are preserved using this. A famous result of word2vec is King - Man + Woman = Queen . This concept has lots other applications as well. Gensim is a library in python which is used to create word2vec models for your corpus. We Learn CBOW- Continuous bowl of words and Skip Gram models to get an intuition about word2vec. Download pretrained word2vec models : https://github.com/jhlau/doc2vec Dataset : https://www.kaggle.com/jiriroz/qa-jokes Find the code GitHub: https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Recommended book for Deep Learning : http://amzn.to/2nXweQS
Views: 74869 The Semicolon
Text mining Lecture 3
 
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Text Mining Lecture 3 1:47 Introduction 2:22 Automated Contract Analysis System (ACAS) 2:48 Literature Contribution 3:59 Literature Review 4:34 ACAS Framework 5:22 Automated Contract Analysis System (ACAS) 5:39 Implementation of ACAS Framework 9:15 Case Study 15:22 Results and Discussions 26:53 Conclusion and Challenges Topic: Textual Risk Disclosure and Investors’Risk Perceptions 42:43 Introduction 46:50 Hypothesis Development 1:02:59 Predictions - Risk Disclosure and Stock Return Volatility 1:06:45 Research Design 1:07:24 Results 1:12:39 Conclusions 1:26:42 Regular Expressions Please subscribe to our channel to get the latest updates on the RU Digital Library. To receive additional updates regarding our library please subscribe to our mailing list using the following link: http://rbx.business.rutgers.edu/subsc…
TF-IDF for Machine Learning
 
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Quick overview of TF-IDF Some references if you want to learn more: Wikipedia: https://en.wikipedia.org/wiki/Tf%E2%80%93idf Scikit's implementation: http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer Scikit's code example for feature extraction: http://scikit-learn.org/stable/modules/feature_extraction.html Stanford notes: http://nlp.stanford.edu/IR-book/html/htmledition/tf-idf-weighting-1.html
Views: 38951 RevMachineLearning
5.1: Intro to Week 5: Text Analysis and Word Counting - Programming with Text
 
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Week 5 of Programming from A to Z focuses on about text-analysis and word counting. In this introduction, I discuss different how word counting and text analysis can be used in a creative coding context. I give an overview of the topics I will cover in this series of videos. Next Video: https://youtu.be/_5jdE6RKxVk http://shiffman.net/a2z/text-analysis/ Course url: http://shiffman.net/a2z/ Support this channel on Patreon: https://patreon.com/codingtrain Send me your questions and coding challenges!: https://github.com/CodingTrain/Rainbow-Topics Contact: https://twitter.com/shiffman GitHub Repo with all the info for Programming from A to Z: https://github.com/shiffman/A2Z-F16 Links discussed in this video: Rune Madsen's Programming Design Systems: http://printingcode.runemadsen.com/ Concordance on Wikipedia: https://en.wikipedia.org/wiki/Concordance_(publishing) Rune Madsen's Speech Comparison: https://runemadsen.com/work/speech-comparison/ Sarah Groff Hennigh-Palermo's Book Book: http://www.sarahgp.com/projects/book-book.html Stephanie Posavec: http://www.stefanieposavec.co.uk/ James W. Pennebaker's The Secret Life of Pronouns: http://www.secretlifeofpronouns.com/ James W. Pennebaker's TedTalk: https://youtu.be/PGsQwAu3PzU ITP from Tisch School of the Arts: https://tisch.nyu.edu/itp Source Code for the all Video Lessons: https://github.com/CodingTrain/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For More Programming from A to Z videos: https://www.youtube.com/user/shiffman/playlists?shelf_id=11&view=50&sort=dd For More Coding Challenges: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6ZiZxtDDRCi6uhfTH4FilpH Help us caption & translate this video! http://amara.org/v/WuMg/
Views: 18167 The Coding Train
Biomedical text mining | Wikipedia audio article
 
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This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Biomedical_text_mining 00:00:43 1 Considerations 00:01:02 1.1 Availability of annotated text data 00:02:45 1.2 Data structure variation 00:03:30 1.3 Uncertainty 00:04:05 1.4 Supporting clinical needs 00:04:33 1.5 Interoperability with clinical systems 00:05:05 1.6 Patient privacy 00:05:25 2 Processes 00:05:42 2.1 Named entity recognition 00:06:30 2.2 Document classification and clustering 00:07:18 2.3 Relationship discovery 00:07:54 2.4 Hedge cue detection 00:08:15 2.5 Claim detection 00:08:54 2.6 Information extraction 00:10:00 2.7 Information retrieval and question answering 00:10:50 3 Resources 00:11:00 3.1 Corpora 00:11:38 3.2 Word embeddings 00:12:15 4 Applications 00:12:40 4.1 Gene cluster identification 00:13:04 4.2 Protein interactions 00:13:48 4.3 Gene-disease associations 00:14:18 4.4 Gene-trait associations 00:14:40 4.5 Protein-disease associations 00:15:01 4.5.1 Applications of phrase mining to disease associations 00:16:27 5 Software tools 00:16:37 5.1 Search engines 00:17:30 5.2 Medical record analysis systems 00:18:36 5.3 Frameworks 00:19:21 5.4 APIs 00:19:45 6 Conferences 00:20:08 7 Journals Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago. Learning by listening is a great way to: - increases imagination and understanding - improves your listening skills - improves your own spoken accent - learn while on the move - reduce eye strain Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone. Listen on Google Assistant through Extra Audio: https://assistant.google.com/services/invoke/uid/0000001a130b3f91 Other Wikipedia audio articles at: https://www.youtube.com/results?search_query=wikipedia+tts Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts Speaking Rate: 0.7643526131329897 Voice name: en-GB-Wavenet-B "I cannot teach anybody anything, I can only make them think." - Socrates SUMMARY ======= Biomedical text mining (including biomedical natural language processing or BioNLP) refers to the methods and study of how text mining may be applied to texts and literature of the biomedical and molecular biology domains. As a field of research, biomedical text mining incorporates ideas from natural language processing, bioinformatics, medical informatics and computational linguistics. The strategies developed through studies in this field are frequently applied to the biomedical and molecular biology literature available through services such as PubMed.
Views: 2 wikipedia tts
Unlocking REF2014:  Text mining to show your impact
 
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Ian Rowlands and Selina Lock (University of Leicester Library) discuss how to use text-mining to demonstrate impact for the REF and research funders. They demonstrate a method using EndNote and a concordance engine.
Views: 25 davidwilsonlibrary
Bayes Classifiers and Sentiment Analysis
 
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In this video, I show how to use Bayes classifiers to determine if a piece of text is "positive" or "negative". In other words, I show you how to make a program with feelings! The kind of classifier I show is called a Bernoulli naive Bayes classifier: https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Bernoulli_naive_Bayes The demo at the beginning of the video can be found at: http://macheads101.com/demos/sentiment/ The source for the demo, as well as for my program to graph the mood over books, can be found here: https://github.com/unixpickle/sentigraph
Views: 8375 macheads101
Sentiment Analysis in other Langugages with Power BI Desktop
 
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In this video I will show you how to do sentiment or text analysis using Power BI. Let's say you run a website to sell handicrafts. Your users submit feedback on your site, and you'd like to find out what users think of your brand, and how that changes over time as you release new products and features to your site. The sentiment analysis service will return a score between 0 and 1 denoting overall sentiment in the input text. Scores close to 1 indicate positive sentiment, while scores close to 0 indicate negative sentiment. As the service used only supports English and Spanish data, in this video I will show you how to translate the text to english to then analyze it with the sentiment service. Donwload Power BI file (Free membership required): https://curbal.com/power-bi-solution-templates-for-facebook Enjoy! Link to Twitter sentiment analysis video: https://www.youtube.com/watch?v=Cof8cEE-0a4 Link to Microsoft Text Analytics Service: https://www.microsoft.com/cognitive-services/en-us/text-analytics-api Link to Google Translate API service: https://cloud.google.com/translate/docs/ Looking for a download file? Go to our Download Center: https://curbal.com/donwload-center SUBSCRIBE to learn more about Power and Excel BI! https://www.youtube.com/channel/UCJ7UhloHSA4wAqPzyi6TOkw?sub_confirmation=1 Our PLAYLISTS: - Join our DAX Fridays! Series: https://goo.gl/FtUWUX - Power BI dashboards for beginners: https://goo.gl/9YzyDP - Power BI Tips & Tricks: https://goo.gl/H6kUbP - Power Bi and Google Analytics: https://goo.gl/ZNsY8l ABOUT CURBAL: Website: http://www.curbal.com Contact us: http://www.curbal.com/contact ▼▼▼▼▼▼▼▼▼▼ If you feel that any of the videos, downloads, blog posts that I have created have been useful to you and you want to help me keep on going, here you can do a small donation to support my work and keep the channel running: https://curbal.com/product/sponsor-me Many thanks in advance! ▲▲▲▲▲▲▲▲▲▲ QUESTIONS? COMMENTS? SUGGESTIONS? You’ll find me here: ► Twitter: @curbalen, @ruthpozuelo ► Google +: https://goo.gl/rvIBDP ► Facebook: https://goo.gl/bME2sB ► Linkedin: https://goo.gl/3VW6Ky
Views: 1624 Curbal
KMeans Text Classification and Document Similarity with C# - Source Code Included
 
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Machine Learning from scratch with C# on Text Classification Blog: http://code-ai.mk/ One of the widely used natural language processing task in different business problems is “Text Classification”. The goal of text classification is to automatically classify the text documents into one or more defined categories. This is a short demonstration on how to cluster/group text documents.We'll use KMeans which is an unsupervised machine learning algorithm. On the video you can see I have collected a lot of Wikipedia articles on three categories and I want to classify them or rank them by similarity. First I process the text, then I create a word dictionary. Based on the word dictionary I will create another frequency dictionary which will contain the frequency at which every word occurs. I will need this to additionally clean up the text from words that do not bring any value/information to the clustering algorithm. Finally I would run KMeans using euclidean distance to find the centroids in the cluster of data. Similar documents would then be grouped together. The project is written from scratch in C#. Source code is also fully available on my blog or upon request. Blog: http://code-ai.mk/
Views: 836 Vanco Pavlevski
"Deep Learning for Text Analysis" by Ananth Iyer and Felix Wyss
 
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"Deep Learning for Text Analysis" by Ananth Iyer and Felix Wyss, Genesys, Inc. IndyPy's Pythology One-Day Conference on Machine Learning, AI, and Genetic Programming. Join the conversation: Meetup: http://indypy.com/ Slack: https://indypy-invite.herokuapp.com ; Twitter: @indypy Thanks to our awesome sponsors: Six Feet Up is your local Python web application and cloud orchestration partner https://sixfeetup.com/ Submit a talk: https://goo.gl/forms/7fOEQjpuWkUo1VWO2 Sponsor IndyPy: https://www.meetup.com/indypy/pages/21793841/Sponsorships/
Views: 292 Six Feet Up Corp
Text mining 2
 
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In this video, we are going to continue to use Text Mining widgets in Orange. In order to download the datasets please go to: https://github.com/RezaKatebi/Crash-course-in-Object-Oriented-Programming-with-Python
Views: 229 DataWiz
Natural Language Processing Tutorial Part 2 | NLP Training Videos | Text Analysis
 
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Natural Language Processing Tutorial Part 2 | NLP Training Videos | Text Analysis https://acadgild.com/big-data/data-science-training-certification?aff_id=6003&source=youtube&account=9LLs2I8_gQQ&campaign=youtube_channel&utm_source=youtube&utm_medium=NLP-part-2&utm_campaign=youtube_channel Hello and Welcome back to Data Science tutorials powered by Acadgild. In the previous video, we came across the introduction part of the natural language processing (NLP) which includes the hands-on part with tokenization, stemming, lemmatization, etc. If You have missed the previous video, kindly click the following link for the better understanding and continuation for the series. NLP Training Video Part 1 - https://www.youtube.com/watch?v=Na4ad0rqwQg In this tutorial, you will be able to learn, • What are the stop keywords and its importance in the process of text analysis? Before going to the core topic let’s understand the difference between Lemmatization and Stemming. Lemmatization Vs Stemming: Lemmatization: • Word representations have meaning • Takes more time than stemming • Use lemmatization when the meaning of words is important for analysis • For example, question answering application. Stemming: • Word representations may not have any meaning • Takes less time • Use stemming when the meaning of words is not important for analysis. • For example, spam detection Kindly go through the hands-on part to learn more about the usage of stop keywords in text analysis. Please like, share and subscribe the channel for more such videos. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 661 ACADGILD
"Data Science" : Text Mining And Clustering Techniques | What Is Clustering | ExcelR
 
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#TextMining #Clustering #Whatistextmining #whatisclustering #datascience(2019) 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. Clustering : Clustering is a Machine Learning technique involving the grouping of data points. It is an unsupervised learning method and a popular technique for statistical data analysis Things you will learn in this video 1) What Is Text Mining? 2)What Is Clustering? 3)Importance of Text MIning 4)Importance of Clustering 5)Terminology & 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 #Clustering #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
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: 474161 sentdex
Evaluating Text Extraction: Apache Tika's™ New Tika-Eval Module - Tim Allison, The MITRE Corporation
 
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Evaluating Text Extraction: Apache Tika's™ New Tika-Eval Module - Tim Allison, The MITRE Corporation Text extraction tools are essential for obtaining the textual content and metadata of computer files for use in a wide variety of applications, including search and natural language processing tools. Techniques and tools for evaluating text extraction tools are missing from academia and industry. Apache Tika™ detects file types and extracts metadata and text from many file types. Tika is a crucial component in a wide variety of tools, including Solr™, Nutch™, Alfresco, Elasticsearch and Sleuth Kit®/Autopsy®. In this talk, we will give an overview of the new tika-eval module that allows developers to evaluate Tika and other content extraction systems. This talk will end with a brief discussion of the results of taking this evaluation methodology public and evaluating Tika on large batches of public domain documents on a public vm over the last two years. About Tim Allison Tim has been working in natural language processing since 2002. In recent years, his focus has shifted to advanced search and content/metadata extraction. Tim is committer and PMC member on Apache PDFBox (since September 2016), and on Apache POI and Apache Tika since (July, 2013). Tim holds a Ph.D. in Classical Studies from the University of Michigan, and in a former life, he was a professor of Latin and Greek.
Views: 2266 The Linux Foundation
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: 1194 PyData
Deepti Ameta | Relation Extraction from Wikipedia articles using DeepDive | PyData Meetup 2
 
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PyData meetups are a forum for members of the PyData community to meet and share new approaches and emerging technologies for data management and analytics. This was the second meet-up of PyData Gandhinagar hosted at IIT Gandhinagar on October 27, 2018. Speaker – Deepti Ameta Bio: Junior Research Fellow at DAIICT Title – Relation Extraction from Wikipedia articles using DeepDive Short Description – Information Extraction is one of the challenging research areas of Computer Science today. The talk focuses on three problems: how to extract the information (relations between two named entities) from unstructured or semi-structured text documents (Wikipedia); to recognize the techniques of storage in Knowledge Base so that the information can be easily utilized and how to construct end to end data pipelines using a tool: DeepDive. A simple example will be used to understand the tool functionality and working. Further focus is on its real world applications.
Views: 410 IIT Gandhinagar
Adele - Someone Like You
 
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Buy/Listen 25: http://smarturl.it/25Album?IQid=yt Buy/Listen 21: http://smarturl.it/Adele21Album?IQid=yt Buy/Listen 19: http://smarturl.it/19Album?IQid=yt Follow Adele on: Facebook - http://facebook.com/adele Twitter - http://twitter.com/adele Instagram - http://instagram.com/adele Subscribe to the Adele VEVO Channel - http://smarturl.it/SubscribeAdele?IQid=yt Visit - www.adele.com
Views: 1354125319 AdeleVEVO
Amy Winehouse - Back To Black
 
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Get AMY OST now: http://po.st/AMYOST3 Listen back to ‘Frank’, ‘Back To Black’, and ‘Lioness: Hidden Treasures’, the Amy Winehouse albums, now: http://po.st/AmyWSpotify Get Amy Winehouse At The BBC: http://po.st/gZgwDm | Amazon http://po.st/4awZ14 The estate of Amy Winehouse is donating the record royalties it receives from the sale of this box set to the Amy Winehouse Foundation. Music video by Amy Winehouse performing Back To Black. (C) 2006 Universal Island Records Ltd. A Universal Music Company. Best of Amy Winehouse: https://goo.gl/3qdV5b Subscribe here: https://goo.gl/MyGNX3
Views: 454450942 AmyWinehouseVEVO
What Is Meant By Sentiment Analysis?
 
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Sentiment analysis definition from financial times lexicon., 7 tor(k 1,k 1,aply(1),,to. Tor(k 1,k 1,aply(1) may 7, 2014 in part two we'll explain how to measure sentiment online. Sentiment analysis and opinion mining mainly focuses on opinions which express or imply jul 27, 2015 text analytics sentiment make up one such pair. Sentiment analysis determines if an expression is positive, negative, or neutral, and to what degree. The problem with sentiment analysis fast company. Sentiment analysis beginners guide social media metrics. According to the oxford dictionary, definition for sentiment analysis is process of computationally identifying opinion mining, which also called analysis, involves building a system collect and categorize opinions about product. Google cloud natural language api creating a sentiment analysis model recursive deep models for semantic compositionality over how works sentdex. Find meaning in the conversations that matter a high level overview of lexalytics' text mining software sentiment analysis tools is type data measures inclination people's opinions through natural language processing (nlp), computational linguistics and analysis, which are used to extract analyze subjective information from web mostly social media similar sources firstly let's look at what. According to the oxford dictionary, definition for sentiment analysis is process of (sa) an ongoing field research in text mining. Sentiment analysis wikipedia. Though the method sep 10, 2011 meaning of opinion itself is still very broad. What is opinion mining (sentiment mining)? Definition from whatis introduction to sentiment analysis. Sentiment analysis and opinion mining uic computer sciencethe importance of sentiment in social media algorithms applications a survey tutorial. Sentiment analysis gives you insight into the emotion behind words mar 17, 2015 firstly let's look at what is sentiment. A sentiment analysis model is used to sentimentwhat sen ment. Dec 16, 2016 this document explains how to create a basic sentiment analysis model using the google prediction api. Product, you might assume a surge in mentions meant it was being well received definition of sentiment analysis. Sentiment analysis (sometimes known as opinion mining or emotion ai) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics systematically identify, extract, quantify, study affective states subjective information jan 26, 2015 what is sentiment how does it work, why should we it? Read our. Automated opinion what is sentiment analysis (sa)? Dream (nadeau et al. A linguistic analysis technique where a body of text is examined to characterise the tonality document. The importance of sentiment analysis in social media results 2day. This website it computes the sentiment based on how words compose meaning of longer phrases there are many ways that people analyze bodies text for or opinions sentence structure behind text, besides pre defin
Views: 132 Another Question II
Content extraction with Apache Tika
 
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Presentation slides available here: http://www.lucenerevolution.org/past_events Apache Tika is a toolkit for detecting and extracting metadata and structured text content from various documents using existing parser libraries. To show how the toolkit can be used with a Lucene or Solr search index, this talk covers Introduction to Apache Tika Full text extraction with Tika Using the Tika-based ExtractingRequestHandler in Solr Integrating Tika directly with Lucene Link extraction for web crawlers Advanced features like forked parsing and the Tika server This talk assumes basic knowledge of Lucene or Solr and of Java programming.
Views: 27235 LuceneSolrRevolution
Solr Analyzer - Text Analysis with Lucene Analyzers, Tokenizers and Filters
 
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This tutorial covers the Solr Analyzer process with Apache Solr Tokenizers and Lucene Filters to grasp text analysis during the Solr indexing and Solr query processes. Find all of the code examples and text from the video here: https://factorpad.com/tech/solr/tutorial/solr-analyzer.html Find the outline to all Solr tutorials here: https://factorpad.com/tech/solr/tutorial/solr-tutorial.html Happy Searching! https://factorpad.com
Views: 4105 FactorPad
Introduction to Text Analytics with R: VSM, LSA, & SVD
 
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Part 7 of this video series includes specific coverage of: – The trade-offs of expanding the text analytics feature space with n-grams. – How bag-of-words representations map to the vector space model (VSM). – Usage of the dot product between document vectors as a proxy for correlation. – Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics. – How LSA is implemented using singular value decomposition (SVD). – Mapping new data into the lower dimensional SVD space. 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 -- Learn more about Data Science Dojo here: https://hubs.ly/H0hD3WT0 Watch the latest video tutorials here: https://hubs.ly/H0hD3X30 See what our past attendees are saying here: https://hubs.ly/H0hD3X90 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 12414 Data Science Dojo
Getting Started with Orange 16: Text Preprocessing
 
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How to work with text in Orange, perform text preprocessing and create your own custom stopword list. For more information on text preprocessing, read the blog: [Text Preprocessing] https://blog.biolab.si/2017/06/19/text-preprocessing/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 22075 Orange Data Mining
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
35:16
There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
Views: 1478 UA German Department
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
35:04
There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
What is CONCEPT MINING? What does CONCEPT MINING mean? CONCEPT MINING meaning & explanation
 
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What is CONCEPT MINING? What does CONCEPT MINING mean? CONCEPT MINING meaning - CONCEPT MINING definition - CONCEPT MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Concept mining is an activity that results in the extraction of concepts from artifacts. Solutions to the task typically involve aspects of artificial intelligence and statistics, such as data mining and text mining. Because artifacts are typically a loosely structured sequence of words and other symbols (rather than concepts), the problem is nontrivial, but it can provide powerful insights into the meaning, provenance and similarity of documents. Traditionally, the conversion of words to concepts has been performed using a thesaurus, and for computational techniques the tendency is to do the same. The thesauri used are either specially created for the task, or a pre-existing language model, usually related to Princeton's WordNet. The mappings of words to concepts are often ambiguous. Typically each word in a given language will relate to several possible concepts. Humans use context to disambiguate the various meanings of a given piece of text, where available machine translation systems cannot easily infer context. For the purposes of concept mining however, these ambiguities tend to be less important than they are with machine translation, for in large documents the ambiguities tend to even out, much as is the case with text mining. There are many techniques for disambiguation that may be used. Examples are linguistic analysis of the text and the use of word and concept association frequency information that may be inferred from large text corpora. Recently, techniques that base on semantic similarity between the possible concepts and the context have appeared and gained interest in the scientific community. One of the spin-offs of calculating document statistics in the concept domain, rather than the word domain, is that concepts form natural tree structures based on hypernymy and meronymy. These structures can be used to produce simple tree membership statistics, that can be used to locate any document in a Euclidean concept space. If the size of a document is also considered as another dimension of this space then an extremely efficient indexing system can be created. This technique is currently in commercial use locating similar legal documents in a 2.5 million document corpus. Standard numeric clustering techniques may be used in "concept space" as described above to locate and index documents by the inferred topic. These are numerically far more efficient than their text mining cousins, and tend to behave more intuitively, in that they map better to the similarity measures a human would generate.
Views: 501 The Audiopedia
Sentiment Analysis - Sirisha
 
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This video describes the implementation of sentimental analysis using Naive Bayes algorithm. This is part of final project of AI course @ UW Instructor: Jeff Clune References: https://www.youtube.com/watch?v=EGKeC2S44Rs https://en.wikipedia.org/wiki/Sentiment_analysis
Views: 19178 UW-AI Class
#FISAMemo Released! Full Text Analysis, and what this means for the "Deep State", #QAnon, #TheStorm
 
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In this video I read through the entire four page FISA Memo, #FISAMemo which is supposed to "destroy the deep state" upon it's release. Unfortunately that was a lot of hype, but it does show criminal activity and a blatant misuse of the FISA system for political personal agendas. Highlighting the case to end the FISA program as it is unconstitutional. FISA MEMO, Full TEXT: http://www.washingtonexaminer.com/fisa-memo-full-text/article/2647942 The Foreign Intelligence Surveillance Act: https://en.wikipedia.org/wiki/Foreign_Intelligence_Surveillance_Act The FISA Courts: https://en.wikipedia.org/wiki/United_States_Foreign_Intelligence_Surveillance_Court These are the Amendments including the 4th & 5th amendment from the Bill of Rights that the FISA system and courts are in direct violation of: The Fourth Amendment: https://www.law.cornell.edu/constitution/fourth_amendment The Fifth Amendment: https://www.law.cornell.edu/constitution/fifth_amendment The Fourteenth Amendment: https://www.law.cornell.edu/constitution/amendmentxiv Paul Ryan is a Zionist scumbag: http://www.jpost.com/Opinion/Columnists/Fundamentally-Freund-Where-does-Paul-Ryan-stand-on-Israel Paul Ryan supports the "Israel is the only democracy in the Middle East" blatant lie. There are many democracies in the Middle East including Syria their neighbor and Lebanon, and Jordan lolz. Basically the only non democracy is Saudi Arabia Israel's ally. Democracy in the Middle East: https://en.wikipedia.org/wiki/Democracy_in_the_Middle_East How many democracies in the Middle East? https://www.quora.com/How-many-democracies-exist-in-the-Middle-East @Run2Christ's video on "The Patriots": https://youtu.be/H7UnDwho8eA Don't fall for the left / right paradigm. There are bigger picture things going on. The Illuminati are playing weak when they are at their strongest. Stay vigilant. Full Show Notes On Steemit: (Link Coming Soon) Read my Steemit Blog! https://steemit.com/@titusfrost My Patreon: https://www.patreon.com/TitusFrost Hit me up on Social Media: * Twitter: @ImperatorTruth: https://twitter.com/ImperatorTruth (When I am un suspended lulz) * FedBook: "The Lost Truth": https://www.facebook.com/TheLostTruthbyDeanFougere/ * Minds.com: "TitusFrost": https://www.minds.com/TitusFrost * YouTube: "Titus Frost": https://www.youtube.com/channel/UCDHrwVzgl-vZ14wWnN1LVjQ * My Published Book: "The Lost Truth": https://www.amazon.com/Lost-Truth-Dean-Fougere/dp/1502511835/ref=tmm_pap_swatch_0?_encoding=UTF8&sr=8 * Gab.ai: TitusFrost: https://gab.ai/TitusFrost * BitChute: https://www.bitchute.com/channel/Titus_Frost/ * LiveStream Channel: https://www.youtube.com/channel/UCN6AnBrQW8PUASrSKeQD1zQ * DTube: https://dtube.video/c/titusfrost * Ong.Social: https://www.ong.social/TitusFrost * Check out my book on OpenLibrary: https://openlibrary.org/works/OL17155637W/The_Lost_
Views: 3048 Titus Frost
How to build a corpus (text formats)
 
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A brief description of how to handle different text formats when building a corpus in corpus linguistics. Feel free to use in your own teaching of corpus linguistics.
Views: 9692 CorpusLingAnalysis
Prof. Xiaozhong Liu, "Heterogeneous Graph + Text Mining Enabled Information Understanding"
 
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Heterogeneous Graph + Text Mining Enabled Information Understanding Prof. Xiaozhong Liu, IU School of Informatics and Computing
Views: 103 IU Computer Vision
Amazing Things NLP Can Do!
 
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In this video I want to highlight a few of the awesome things that we can do with Natural Language Processing or NLP. NLP basically means getting a computer to understand text and help you with analysis. Some of the major tasks that are a part of NLP include: · Automatic summarization · Coreference resolution · Discourse analysis · Machine translation · Morphological segmentation · Named entity recognition (NER) · Natural language generation · Natural language understanding · Optical character recognition (OCR) · Part-of-speech tagging · Parsing · Question answering · Relationship extraction · Sentence breaking (also known as sentence boundary disambiguation) · Sentiment analysis · Speech recognition · Speech segmentation · Topic segmentation and recognition · Word segmentation · Word sense disambiguation · Lemmatization · Native-language identification · Stemming · Text simplification · Text-to-speech · Text-proofing · Natural language search · Query expansion · Automated essay scoring · Truecasing Let’s discuss some of the cool things NLP helps us with in life 1. Spam Filters – nobody wants to receive spam emails, NLP is here to help fight span and reduce the number of spam emails you receive. No it is not yet perfect and I’m sure we still all still receive some spam emails but imagine how many you’d get without NLP! 2. Bridging Language Barriers – when you come across a phrase or even an entire website in another language, NLP is there to help you translate it into something you can understand. 3. Investment Decisions – NLP has the power to help you make decisions for financial investing. It can read large amounts of text (such as news articles, press releases, etc) and can pull in the key data that will help make buy/hold/sell decisions. For example, it can let you know if there is an acquisition that is planned or has happened – which has large implications on the value of your investment 4. Insights – humans simply can’t read everything that is available to us. NLP helps us summarize the data we have and pull out meaningful information. An example of this is a computer reading through thousands of customer reviews to identify issues or conduct sentiment analysis. I’ve personally used NLP for getting insights from data. At work, we conducted an in depth interview which included several open ended response type questions. As a result we received thousands of paragraphs of data to analyze. It is very time consuming to read through every single answer so I created an algorithm that will categorize the responses into one of 6 categories using key terms for each category. This is a great time saver and turned out to be very accurate. Please subscribe to the YouTube channel to be notified of future content! Thanks! https://en.wikipedia.org/wiki/Natural_language_processing https://www.lifewire.com/applications-of-natural-language-processing-technology-2495544
Views: 7013 Story by Data
The Curious Case of the Green Children of Woolpit
 
05:59
Check my other channel Highlight History: https://www.youtube.com/channel/UCnb-VTwBHEV3gtiB9di9DZQ →Some of our favorites: https://www.youtube.com/playlist?list=PLR0XuDegDqP10d4vrztQ0fVzNnTiQBEAA →Subscribe for new videos every day! https://www.youtube.com/user/TodayIFoundOut?sub_confirmation=1 Follow Simon on social media: https://twitter.com/SimonWhistler https://www.instagram.com/simonwhistler/ Never run out of things to say at the water cooler with TodayIFoundOut! Brand new videos 7 days a week! More from TodayIFoundOut: What Happens If You are Sleep Walking and You Kill Someone? https://youtu.be/ggvdSL8ILKM Killing a Cat in Court https://youtu.be/UK3OWsDlKm4 In this video: English folklore is filled with green people – the Green Knight, green fairies, the Green Man and Jack-in-the-Green. Two of the smallest were the Green Children of Woolpit. Want the text version?: http://www.todayifoundout.com/index.php/2014/02/green-children-woolpit/ Sources: http://jn.nutrition.org/content/125/7/1822.full.pdf http://en.wikipedia.org/wiki/Green_children_of_Woolpit http://brian-haughton.com/articles/green-children-of-woolpit/ http://www.mysteriousbritain.co.uk/england/suffolk/folklore/the-green-children-of-woolpit.html http://eclectariumshuker.blogspot.com/2012/11/the-green-children-of-woolpit.html https://www.google.com/maps/place/Suffolk/@52.2408239,1.0515024,9z/data=!3m1!4b1!4m2!3m1!1s0x47d81562eecf1ae1:0xb8cf4391eed96afa http://ihrrblog.org/2010/06/17/groundwater-arsenic-poisoning-in-bangladesh-an-interview-with-dr-manzurul-hassan/ http://www.royal.gov.uk/Home.aspx http://www.royal.gov.uk/HistoryoftheMonarchy/KingsandQueensofEngland/TheAngevins/HenryIICurtmantle.aspx http://en.wikipedia.org/wiki/Fornham_St_Martin https://www.english-heritage.org.uk/daysout/properties/grimes-graves-prehistoric-flint-mine/ https://en.wikipedia.org/wiki/Hypochromic_anemia
Views: 179965 Today I Found Out
The Weeknd - The Hills
 
03:55
The Weeknd - The Hills (Official Video) Download Song: http://theweeknd.co/BeautyBehindTheMadness Taken from the new album Beauty Behind The Madness Stream/Share on Spotify: https://open.spotify.com/track/2HNcNd5RPZ7DSRNbIl6JsP Connect with The Weeknd: http://www.instagram.com/abelxo http://www.twitter.com/TheWeeknd http://www.facebook.com/theweeknd http://www.theweeknd.com Directed by Grant Singer Produced by Nathan Scherrer Production Company: FREENJOY, INC. Music video by The Weeknd performing The Hills. © 2015 The Weeknd XO, Inc., Manufactured and Marketed by Republic Records, a Division of UMG Recordings, Inc. http://www.vevo.com/watch/USUMV1500241 Best of The Weeknd: https://goo.gl/76DXjL Subscribe here: https://goo.gl/GWXGWM
Views: 1358728143 TheWeekndVEVO
1.1: Introduction - Programming with Text
 
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Welcome to Programming from A to Z! "Programming from A to Z" is a course I teach at ITP (http://itp.nyu.edu). This playlist is anyone who would like to follow along online. Each week, I'll release videos on a new topic. Here is the course description. This course focuses on programming strategies and techniques behind procedural analysis and generation of text-based data. We'll explore topics ranging from evaluating text according to its statistical properties to the automated production of text with probabilistic methods to text visualization. Students will learn server-side and client-side JavaScript programming and develop projects that can be shared and interacted with online. There will be weekly homework assignments as well as a final project. Course url: http://shiffman.net/a2z/ Next Video: https://youtu.be/d3OcFexe9Ik Support this channel on Patreon: https://patreon.com/codingtrain Send me your questions and coding challenges!: https://github.com/CodingTrain/Rainbow-Topics Contact: https://twitter.com/shiffman GitHub Repo with all the info for Programming from A to Z: https://github.com/shiffman/A2Z-F16 Links discussed in this video: ITP from Tisch School of the Arts: https://tisch.nyu.edu/itp Influences and Inspiration for the Programming from A to Z class: Jackson Mac Low's Wikipedia Page: https://en.wikipedia.org/wiki/Jackson_Mac_Low Nick Montfort: http://nickm.com/ Allison Parrish: http://www.decontextualize.com/ Kate Compton's Tracery: https://github.com/galaxykate/tracery Addie Wagenknecht: http://www.placesiveneverbeen.com/ Lynn Cherny: http://www.ghostweather.com/bio.html Darius Kazemi: http://tinysubversions.com/ Eyeo Festival on Vimeo: https://vimeo.com/eyeofestival Source Code for the all Video Lessons: https://github.com/CodingTrain/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For More Programming from A to Z videos: https://www.youtube.com/user/shiffman/playlists?shelf_id=11&view=50&sort=dd For More Coding Challenges: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6ZiZxtDDRCi6uhfTH4FilpH Help us caption & translate this video! http://amara.org/v/V91D/
Views: 125669 The Coding Train
Mark Ronson - Nothing Breaks Like a Heart (Official Video) ft. Miley Cyrus
 
04:00
Mark Ronson feat. Miley Cyrus - Nothing Breaks Like a Heart NEW ALBUM 💔 LATE NIGHT FEELINGS 💔 JUNE 21 ft. Miley Cyrus, Camila Cabello, Alicia Keys, King Princess + more. Presave here: http://ron.sn/LNFpre Vinyls, 8-tracks + more here: ron.sn/LNFStorePreOrderYTD LATE NIGHT FEELINGS ft Lykke Li OUT NOW: http://ron.sn/LateNightFeelings Listen/Download to 'Nothing Breaks Like a Heart': http://smarturl.it/NBLAH?IQid=yt ------------------- Follow on Spotify: http://ron.sn/stream/spotify Listen on Apple Music: http://ron.sn/stream/applemusic Listen on Amazon: http://ron.sn/stream/amazon   Listen to more music from Mark Ronson here: http://ron.sn/stream   Follow Mark Ronson Newsletter: http://ron.sn/join Website: http://markronson.co.uk/ Facebook: https://facebook.com/MarkRonson Twitter: https://twitter.com/MarkRonson Instagram: https://instagram.com/iamMarkRonson/ Follow Miley Cyrus https://www.mileycyrus.com Instagram - http://instagram.com/mileycyrus Twitter - http://twitter.com/mileycyrus Facebook - http://facebook.com/mileycyrus ------------------- Directed by We Are From LA Produced by Iconoclast Executive Producer: Romain Gavras Producer: Natan Schottenfels Line Producer: Mélodie Buchris Direct or of Photography: Benoit Debie Editor: Simon Colin VFX: Mathmatic Kiev Production Service: Limelite #MarkRonson #MileyCyrus #NothingBreaksLikeAHeart
Views: 119246931 MarkRonsonVEVO
Intro to Text Mining - Getting Started with Computational Methods
 
01:18
Rada Mihalcea, Professor in the Computer Science and Engineering department at the University of Michigan, suggests a few basic steps that social scientists can take to start engaging with computational methods. Rada is an instructor on SAGE Campus’ Introduction to Text Mining for Social Scientists online course. Find out more: https://campus.sagepub.com/introduction-to-text-mining-for-social-scientists/
Views: 111 SAGE Ocean
Using network science and text analytics to produce surveys in a scientific topic
 
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3D visualization of the citation network obtained by querying the term "Complex Network" in the Web of Science® database. The full paper can be accessed in: http://arxiv.org/abs/1506.05690 . Using network science and text analytics to produce surveys in a scientific topic Journal of Informetrics 10 (2016) pp. 487-502 The presented visualization was created using a new network visualization software, currently being developed by researchers of the Luciano's Group: http://cyvision.ifsc.usp.br/Cyvision/?page=SOFTWARE&subpage=NETWORKS3D More information about complex networks can be found in our papers: Advances in Physics, V. 60(3) p. 329-412 (2011) https://arxiv.org/abs/0711.3199 São Carlos Institute of Physics University of São Paulo Brazil
QDA Miner Lite - Free Qualitative Data Analysis Software
 
07:29
In this video, we are going to show you how to use QDA Miner Lite to code and analyze your documents and images. QDA Miner Lite is a free qualitative data analysis software made by Provalis Research.