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: 76445 Linguamatics
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: 24588 DataCamp
Carolyn Rose discusses text mining conceptual overview of techniques for week 7 of DALMOOC.
Views: 1644 Data Analytics and Learning MOOC
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
Views: 149401 Siraj Raval
#iiutor #English #LanguageTechniques https://www.iitutor.com Techniques for Analysing Visual Texts • You probably have a good understanding of language techniques. • To analyse images, you need to understand the elements of an image. • Techniques help you to deconstruct the image and see the importance of things you might not have noticed before! Directional Terms • Layout: The way in which images or text blocks are arranged on a page in relation to each other. • You may also like to talk about the composition and where the eye is led. • This is useful for book covers, magazines and advertisements. • Background – the furthest distance away, often what is least important. • Mid-ground – the middle of the image if the image were 3D. • Foreground – the front of the image, often the focus point for the viewer. Things being emphasised are placed here. Image Relationships Juxtaposition: • Deliberately putting two objects together to make an association or relationship. • This often shows why they’re similar. Contrast: • To put two very different things together. • To show why they’re different. • NOTE: some people wrongly use contrast and juxtaposition interchangeably. Focus: • The place on the page your eye is drawn to when you first look at the picture. • The focus is often close to the centre of the frame. Frame: • What’s at the edge of the picture? • Why was it included, or why wasn’t it left out? • Usually helps to create a rectangular “cropped” feel to the image. Vector: • Lines on the page create a direction for your eye to travel in a specific order. • Something you follow often without even realising. • Similar to “where the eye is led” or a “directional line.” Colour Techniques • Vivid colour: like a dream or a child’s view, strong emotions. • Murky colour: something is wrong or dirty or ordinary. • Bright colour: lots of energy, new. • Pastel colour: gentle, dreamy, babies. • Dark colours: mysterious, evil, scary, unknown, strong emotion. • Watery colours: emotional, impression. • Red: danger, emotions like love and hate, fear, battle, blood, attention-seeking. Lighting Techniques • Bold: well defined lines or blocks of strong colour. • Stark: lots of dark and light contrast, sharp angles → cruel, mean, professional, clinical, or scientific. • Gradation: one spectrum to another gradually. • Implies change, loss, or distance. • Lighting effects: usually used for photographs only. • Light and shadow in the photo can help to place importance on the objects. • e.g. the lightest part of the picture is usually looked at first, as though it’s in a spotlight on a stage. Texture Techniques • Rough: looks natural, unfinished, unrefined, old etc. • Smooth: looks even, smooth, simple. Can be feminine, or sleek looking, or commercial or new. • Organic: round and flowing shapes and curves, looks natural, not sharp. • Geometric: looks computer-generated or not-real or unnatural, contrived etc. • Line: a directional technique – “the use of wood grains creates a directional line across the page for the eye to follow.”
Views: 12964 iitutor.com
Text Mining and Analytics Intro into Text Mining and Analytics - Chapter 1 This video tutorials cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications. analytics | analytics tools | analytics software | data analysis programs | data mining tools | data mining | text analytics | strucutred data | unstructured data |text mining | what is text mining | text mining techniques More Articles, Scripts and How-To Papers on http://www.aodba.com
Views: 360 AO DBA
Par Mr Luc Grivel
Views: 172 DocExpo Maroc
ExcelR: The imposition of identity on input data, such as speech, images, or a stream of text, by the recognition and delineation of patterns it contains and their relationships. Things you will learn in this video 1)Introduction to pattern recognition 2)Why text mining? 3)Importance of text mining 4)Terminology & 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 Introduction to data mining techniques click here https://goo.gl/BQSFGo For Introduction to data science demo click here https://goo.gl/2vkFjq #ExcelRSolutions #patternrecognition# whatistextmining #Introductiontopatternrecognition #NormalDistribution #DataScienceCertification #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
This is a webinar I delivered as a part of a webinar series entitled "We are all Social Things" organized by the IS department at King Saud University - Female Section The recording started a bit late, but you will be able to follow.
Views: 766 Ibrahim Almosallam
STATISTICA Text Miner out of the box does not include functionality to find n-grams. In this video I show how to use the tm and RWeka packages to find frequent phrases (n-grams) and return the results to STATISTICA so they can be used in a text mining project.
Views: 643 DManswers
Matthew Jockers, University of Nebraska-Lincoln assistant professor of English, combines computer programming with digital text-mining to produce deep thematic, stylistic analyses of literary works throughout history -- an intensely data-driven process he calls macroanalysis. It's opening up new methods for literary theorists to study literature. http://research.unl.edu/annualreport/2013/pioneering-new-era-for-literary-scholarship/ http://research.unl.edu/
Views: 2535 University of Nebraska–Lincoln
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: 434693 sentdex
The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and initial data cleaning – Some initial data analysis, feature engineering, and data visualization About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5JLp0 See what our past attendees are saying here: https://hubs.ly/H0f5JZl0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 65424 Data Science Dojo
Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5. Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research: Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772. Learn more about Dr. Leslie Curry http://publichealth.yale.edu/people/leslie_curry.profile Learn more about the Yale Global Health Leadership Institute http://ghli.yale.edu
Views: 158017 YaleUniversity
This video shows how you can translate text documents into a word cloud using Excel. WordMap performs basic text mining (stemming and filtering out prepositions, pronouns, common words, etc) on a collection of documents and then maps the extracted topics into a word cloud that reflects not only topic frequency but also affinity. WordMap can perform basic text-mining in English, Spanish and Portuguese. All this in Excel. Table of Contents: 00:00 - Introduction 05:15 - Choosing the map 05:44 - WordMap (MDS) 07:26 - DocuMap & WordMap
Views: 55 KamakuraAnalyticTools
Key Takeaways for the session : Breaking junk using formula and generate reports VBA to manipulate data in required format Data extraction from external files Who should attend? People from any domain who work on data in any form. Good for Engineers, Leads, Managers, Sales people, HR, MIS experts, Data scientists, IT Support, BPO, KPO etc. Feel free to write me at [email protected]
Views: 24456 xtremeexcel
DSTK - Data Science Toolkit offers Data Science softwares to help users in data mining and text mining tasks. DSTK follows closely to CRISP DM model. DSTK offers data understanding using statistical and text analysis, data preparation using normalization and text processing, modeling and evaluation for machine learning and statistical learning algorithms. DSTK Text Explorer helps user to do text mining and text analytics task easily. It allows text processing using stopwords, stemming, uppercase, lowercase and etc. It also has features in sentiment analysis, text link analysis, name entity, pos tagging, text classification using stanford nlp classifier. It allows data scraping from images, videos, and webscraping from websites. For more information, visit: http://dstk.tech
Views: 3628 SVBook
This webinar introduces PoolParty Semantic Suite, the main software product of Semantic Web Company (SWC), one of the leading providers of graph-based metadata, search, and analytic solutions. PoolParty is a world-class semantic technology suite that offers sharply focused solutions to your knowledge organization and content business. PoolParty is the most complete semantic middleware on the global market. You can use it to enrich your information with valuable metadata to link your business and content assets automatically. This webinar focuses on the text-mining and entity- / text extraction capability of PoolParty Semantic Suite that is used for: • support of the continuous modelling of industrial knowledge graphs (as a supervised learning system) • for entity linking and data integration • for classification and semantic annotation mechanisms • and thereby for downstream applications like semantic search, recommender systems or intelligent agents The webinar presents and explains these features in the PoolParty software environment, shows demos based on real world use cases and finally showcases 3rd party integrations (e.g. into Drupal CMS).
Views: 159 AIMS CIARD
Social Analytics and Text Mining, Lecture of Prof. Prasenjit Mitra, College of Information Sciences and Technology, Pennsylvania State University, "Information Extraction and Text Mining from Large Document Corpora" Data Mining for Business Intelligence - Bridging the Gap Ben-Gurion University of the Negev
Views: 3020 BenGurionUniversity
Take this course on edX: https://www.edx.org/course/text-mining-analytics-delftx-txt1x#! ↓ More info below. ↓ Follow on Facebook: https://www.facebook.com/edX Follow on Twitter: https://www.twitter.com/edxonline Follow on YouTube: https://www.youtube.com/user/edxonline About this course The knowledge base of the world is rapidly expanding, and much of this information is being put online as textual data. Understanding how to parse and analyze this growing amount of data is essential for any organization that would like to extract valuable insights and gain competitive advantage. This course will demonstrate how text mining can answer business related questions, with a focus on technological innovation. This is a highly modular course, based on data science principles and methodologies. We will look into technological innovation through mining articles and patents. We will also utilize other available sources of competitive intelligence, such as the gray literature and knowledge bases of companies, news databases, social media feeds and search engine outputs. Text mining will be carried out using Python, and could be easily followed by running the provided iPython notebooks that execute the code. FAQ Who is this course for? The course is intended for data scientists of all levels as well as domain experts on a managerial level. Data scientists will receive a variety of different toolsets, expanding knowledge and capability in the area of qualitative and semantic data analyses. Managers will receive hands-on oversight to a high-growth field filled with business promise, and will be able to spot opportunities for their own organization. You are encouraged to bring your data sources and business questions, and develop a professional portfolio of your work to share with others. The discussion forums of the course will be the place where professionals from around the world share insights and discuss data challenges. How will the course be taught? The first week of the course describes a range of business opportunities and solutions centered around the use of text. Subsequent weeks identify sources of competitive intelligence, in text, and provide solutions for parsing and storing incoming knowledge. Using real-world case studies, the course provides examples of the most useful statistical and machine learning techniques for handling text, semantic, and social data. We then describe how and what you can infer from the data, and discuss useful techniques for visualizing and communicating the results to decision-makers. What types of certificates does DelftX offer? Upon successful completion of this course, learners will be awarded a DelftX Professional Education Certificate. Can I receive Continuing Education Units? The TU Delft Extension School offers Continuing Education Units for this course. Participants of TXT1x who successfully complete the course requirements will earn a Certificate of Completion and are eligible to receive 2.0 Continuing Education Units (2.0 CEUs) How do I receive my certificate and CEUs? Upon successful completion of the course, your certificate can be printed from your dashboard. The CEUs are awarded separately by the TU Delft Extension School. ------- LICENSE The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.
Views: 2974 edX
#iitutor #English #LanguageTechniqes https://www.iitutor.com Many of the techniques in this video are basic, but it’s essential you are able to identify and discuss all of them to aid in English studies and how you analyse texts. Alliteration / Assonance, Hyperbole, Tone / Mood, Imagery, Repetition / Rhyme, Onomatopoeia. Alliteration is the repetition of consonant sounds. Assonance is the repetition of vowel sounds. e.g. Peter Pauper picked a pair of pickled pears. Paul approached the proposal with apprehension. Hyperbole is when a writer exaggerates an idea, person, a thing or an event for dramatic effect. e.g. He was almost knee-high to an ant. I could touch the sky I was so happy. Tone / mood refers to how reading or viewing something makes an audience feel. Usually can be described with a ‘feeling’ word. e.g The author adopts a sombre tone to represent her loss. The film uses colour and music create a positive mood. Imagery refers to adjectives, images or descriptions chosen by an author to represent an idea or event. e.g. Author uses imagery such as crooked trees and sneering gargoyles to represent the house as ‘haunted’. Repetition refers to an idea or feature being used more than once. Rhyme refers to how sounds are repeated in words. e.g. The author repeats the cat to show her childhood. She uses an ABAB rhyming pattern to quicken the reader’s pace. Onomatopoeia refers to how words can be used to represent sounds or noises themselves. e.g. ‘Boom’, ‘Crash’, ‘Roar’. The muttering became a roar and like the crack of thunder they cheered. Summary: It is essential to be able to identify techniques to succeed in your English studies. In preparation for any exam, ensure you know how to identify and name each technique.
Views: 5533 iitutor.com
Name to Structure (N2S) is a mature English name-to-structure conversion API development by ChemAxon. It is the underlying technology used in ChemAxon's chemical text mining tool D2S (Document to Structure). D2S can extract chemical information from individual file or a document repository system, such as Documentum and SharePoint. To accommodate the fast growing Chinese scientific literature, ChemAxon has recently developed CN2S (Chinese Name to Structure). In this presentation, we will demonstrate how CN2S can convert Chinese chemical names to structures, and its application in Chinese text mining.
Views: 269 ChemAxon
Analyze thousands of texts at scale with Machine Learning. Stop spending time tagging every single row of text, let AI do the work for you. Eliminate manual and repetitive tasks when processing rows of text: - Save time by automatically tagging text in Google Sheets. - 100x faster than doing it with humans. - 50x cheaper than doing it with humans. Make the analysis of your spreadsheets more efficient: - Ensure consistent tagging criteria, 24/7, no errors. - Get insights faster from your data with automated analysis. - Learn from your data with customized tags. Obtain reporting and insights: - Directly integrated into Google Sheets. - Build customized reports with Google Sheets or your own BI tools. - Quick start with pre-made models such as sentiment analysis or keyword extraction. Add MonkeyLearn to Google Sheets: https://chrome.google.com/webstore/detail/monkeylearn/cedpjjdkkbclbllppflfmoacfcjpmdng/ Request a demo: https://monkeylearn.typeform.com/to/nneRwV Learn more about MonkeyLearn: https://monkeylearn.com/
Views: 118 MonkeyLearn
Natural Language Processing Tutorial Part 3 | NLP Training Videos | Text Analysis https://acadgild.com/big-data/data-science-training-certification?aff_id=6003&source=youtube&account=af4C8OhoWlQ&campaign=youtube_channel&utm_source=youtube&utm_medium=NLP-part-3&utm_campaign=youtube_channel Hello and Welcome back to Data Science tutorials powered by Acadgild. In the previous videos, we came across the introduction part of the natural language processing (NLP) which includes the hands-on part with tokenization, stemming, lemmatization, and stop keywords. 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 NLP Training Video Part 2 - https://www.youtube.com/watch?v=9LLs2I8_gQQ In this tutorial, you will be able to learn, how to apply stop_keywords and stemming, and how to apply stop_keywords and lemmatization. Kindly go through the hands-on part to learn more about the applications. 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: 270 ACADGILD
conTEXT allows to semantically analyze text corpora (such as blogs, RSS/Atom feeds, Facebook, G+, Twitter or SlideWiki.org decks) and provides novel ways for browsing and visualizing the results.
Views: 1397 Ali Khalili
This elective course is offered to Tepper School MBA students. The instructor is Dokyun Lee, Assistant Professor of Business Analytics. Video Transcript: There has been this big buzz about big data, right? Big data, I mean any combination of high resolution, unstructured, large volumes of data, and lots of interest on how firms can utilize this data. Very simple algorithm used to aggregate. In my course, I teach the basics of techniques to deal with big data. And the biggest part of it is machine learning, text mining, computer vision algorithm. I teach the basic concept of machine learning for the first 50% of the course. And then the remaining 50%, based on these fundamental concepts in machine learning, how a business, or for example, managers can utilize this for their business. And because it's such an exciting field, it's always growing, always changing. It's real time. You know, every day new techniques or new ways of using these techniques are featured and used. I cover real-world examples of these techniques.
Views: 431 TepperCMU
We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 163326 Timothy DAuria
Critical Analysis Techniques Career Stage - Proficient Main Focus area - 2.5 School - The University of Adelaide A teacher delivers a lesson on critical reading strategies to her peers. She links these strategies to the critical literacy skills required of academic study. In presenting her lesson, she provides examples of language use that allow her 'students' to identify how language affects understanding and perception. By introducing criteria, by which students can analyse texts, the teacher reinforces basic critical thinking techniques appropriate for the skill levels of students undertaking the University's Preparatory Program. http://www.aitsl.edu.au/australian-professional-standards-for-teachers/illustrations-of-practice/detail?id=IOP00289
Views: 3371 AITSL
The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Text and video (including audio) © Kent Löfgren, Sweden
Views: 703080 Kent Löfgren
Today we’re going to talk about how computers understand speech and speak themselves. As computers play an increasing role in our daily lives there has been an growing demand for voice user interfaces, but speech is also terribly complicated. Vocabularies are diverse, sentence structures can often dictate the meaning of certain words, and computers also have to deal with accents, mispronunciations, and many common linguistic faux pas. The field of Natural Language Processing, or NLP, attempts to solve these problems, with a number of techniques we’ll discuss today. And even though our virtual assistants like Siri, Alexa, Google Home, Bixby, and Cortana have come a long way from the first speech processing and synthesis models, there is still much room for improvement. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 176006 CrashCourse
You would surely like to see: http://www.buzzle.com/articles/examples-of-propaganda-techniques.html Kings, political leaders, and even advertisers have been using propaganda to influence behavior for centuries now. The types of propaganda included in this video are the ones that the Institute for Propaganda Analysis has identified as the most common types of propaganda methods. You will see the meaning and examples of various techniques like bandwagon, card stacking, name calling, plain folks, testimonial, and transfer. The examples of propaganda techniques used in this video make it easy for you to understand the concepts behind them.
Views: 11713 Buzzle
Welcome to the 1st Episode of Learn Python for Data Science! This series will teach you Python and Data Science at the same time! In this video we install Python and our text editor (Sublime Text), then build a gender classifier using the sci-kit learn library in just about 10 lines of code. Please subscribe & share this video if you liked it! The code for this video is here: https://github.com/llSourcell/gender_classification_challenge I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Download Python here: https://www.python.org/downloads/ Download Sublime Text here: https://www.sublimetext.com/3 Some Great simple sci-kit learn examples here: https://github.com/chribsen/simple-machine-learning-examples and the official scikit website: http://scikit-learn.org/ Highly recommend this online book as supplementary reading material: https://learnpythonthehardway.org/book/ Wondering when to use which model? This chart helps, but keep in mind deep neural nets outperform pretty much any model given enough data and computing power. so use these when you don't have access to loads of data and compute: http://scikit-learn.org/stable/tutorial/machine_learning_map/ Thank you guys for watching! Subscribe, like, and comment! That's what keeps me going. Feel free to 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
Views: 476624 Siraj Raval
Talk by Ekaterina Kochmar, University of Cambridge, at the Cambridge Coding Academy Data Science Bootcamp: https://cambridgecoding.com/datascience-bootcamp
Views: 137737 Cambridge Coding Academy
ExcelR Data Mining Tutorial for Beginners 2018 - Introduction to various Data mining unsupervised techniques namely Clustering, Dimension Reduction, Association Rules, Recommender System or Collaborative filtering, Network Analytics. Things you will learn in this video 1)What is DataMining 2)DataMining in Nutshell 3)Types of methods 4)DataMining process 5)Approaches 6)Types of Clustering Algorithms To buy eLearning course on DataScience click here https://goo.gl/oMiQMw To enroll for the virtual online course click here https://goo.gl/m4MYd8 To register for classroom training click here https://goo.gl/UyU2ve SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For Introduction to Clustering Analysis clicks here https://goo.gl/wuXN48 For Introduction to K-mean clustering click here https://goo.gl/PYqXRJ #ExcelRSolutions #DataMining#clusteringTechniques #datascience #datasciencetutorial #datascienceforbeginners #datasciencecourse ----- 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
Watch Shaun's Smrt Live Class live for free on YouTube every Thursday at 17 00 GMT (17 00 GMT = https://goo.gl/cVKe0m). Become a Premium Subscriber: http://www.smrt.me/smrt/live Premium Subscribers receive: - Two 1-hour lessons per week with a Canadian or American teacher - Video-marked homework & assignments - Quizzes & exams - Official Smrt English Certification - Weekly group video chats In this video, we will discuss how to write a successful summary in academic English. Students will learn the important do's and don'ts of summary writing and be able to read a text and summarize it more effectively. Join the Facebook group: http://www.facebook.com/groups/leofgroup If you would like to support the stream, you can donate here: https://goo.gl/eUCz92 Exercise: http://smrtvideolessons.com/2013/06/26/how-to-write-a-summary/ Learn English with Shaun at the Canadian College of English Language! http://www.canada-english.com
Views: 1083141 Smrt English