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Text Mining with Tidy Data Principles and Count-based Methods
 
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Speaker: Julia Silge, StackOverflow Presented on November 30, 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).
Data Mining and Text Mining with John Elder
 
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Analytics 2014 Conference Keynote Conference John Elder of Elder Research explains the top three challenges of data mining and text mining, and how to solve them. Learn more about Analytics 2014 at http://www.sas.com/analyticsseries/us/
Views: 1149 SAS Software
Text Mining, the Tidy Way
 
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Delivered by Julia Silge (Stack Overflow) at the 2017 New York R Conference on April 21st and 22nd at Work-Bench.
Views: 3112 Work-Bench
Text Analysis in Power BI with Cognitive services with Leila Etaati
 
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Abstract: Data that we collected always is not about numbers and structured data. In any organization, there is a need to analyze the text data such as customer comments, extract the primary purpose of a call from its scripts, detect the language of customer feedback and translate it and so forth. To address this issue, Microsoft Cognitive Services provides a set of APIs, SDKs, and services available to developers to do text analysis without writing R or Python codes. In this session, I will explain what is text analysis such as sentiment analysis, key phrase extraction, Language detection and so forth. Next, the process of text analysis in Power BI using cognitive services will be demonstrated. Follow us on Twitter - https://twitter.com/mspowerbi More questions? Try asking the Power BI Community @ https://community.powerbi.com/
Views: 7114 Microsoft Power BI
[Webinar Recording] Best Practices for Large Scale Text Mining Process
 
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Large textual collections are a precious source of information which are hard to organise and access due to their unstructured and heterogeneous nature. With the help of text mining and text analytics we can facilitate the information extraction that mines this hidden knowledge. In this webinar, Ivelina Nikolova, Ph.D., shared best practices and text analysis examples from successful text mining process in domains like news, financial and scientific publishing, pharma industry and cultural heritage. View more on https://ontotext.com Connect with Ontotext - Ontotext YouTube channel subscription: https://goo.gl/VPK5J7 Ontotext Google+: https://www.google.com/+Ontotext Ontotext Facebook: https://www.facebook.com/Ontotext Ontotext Twitter: https://twitter.com/ontotext Ontotext LinkedIn: https://www.linkedin.com/company/ontotext-ad
Views: 109 Ontotext
Lexalytics User Group Conference - Brandon Kane of Angoss on Text Mining and Predictive Analytics
 
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Brandon Kane of Angoss discusses how to use the Lexalytics Salience engine to power their Predictive Analysis work. Through the use of text mining and sentiment analysis, Brandon demonstrates how Angoss evolved their business and increased their productivity, as well as their competitiveness. http://www.lexalytics.com/ http://www.angoss.com/
Views: 568 Lexalytics
GOTO 2013 • Elasticsearch - Beyond Full-text Search • Alex Reelsen
 
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This presentation was recorded at GOTO Aarhus 2013 http://gotocon.com Alex Reelsen - Software Engineer at Elasticsearch ABSTRACT Elasticsearch is the leading real-time, distributed, open source search and analytics engine. In addition to providing a highly scalable full-text search engine, users choose Elasticsearch to build sophisticated real-time analytics applications. Recently recommended by Thoughtworks as the #1 technology platform to adopt, Elasticsearch users include SoundCloud, Github, Foursquare and StackOverflow. We'll show some of the real world examples of the cool stuff they are doing using Elasticsearch. https://twitter.com/gotocon https://www.facebook.com/GOTOConference http://gotocon.com
Views: 16062 GOTO Conferences
Text Mining and Analytics | DelftX on edX | Course About Video
 
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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: 2941 edX
Text Analysis - Intro to Computer Science
 
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This video is part of an online course, Intro to Computer Science. Check out the course here: https://www.udacity.com/course/cs101.
Views: 1466 Udacity
Introduction to Text Analytics with R: Data Pipelines
 
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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 Part 3 of this video series provides an introduction to the video series and includes specific coverage: - Exploration of textual data for pre-processing “gotchas” - Using the quanteda package for text analytics - Creation of a prototypical text analytics pre-processing pipeline, including (but not limited to): tokenization, lower casing, stop word removal, and stemming. - Creation of a document-frequency matrix used to train machine learning models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam... The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- 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/H0f5K0c0 See what our past attendees are saying here: https://hubs.ly/H0f5JN90 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 14960 Data Science Dojo
Big Data and text-mining
 
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Views: 170 ESILV
Repository Data Mining on GitHub @ WeAreDevelopers Conference 2017
 
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Visit the largest developers congress in Europe: WeAreDevelopers World Congress, 16 - 18 May 2018 in Vienna, Austria. https://www.wearedevelopers.com/congress/ Maxim Schuwalow, Fabian Richter, Tobias Ludwig and Johannes Nicolai from GitHub https://www.wearedevelopers.com/ Facebook: https://www.facebook.com/wearedevelopers.org/ Twitter: https://www.twitter.com/wearedevs/ LinkedIn: https://www.linkedin.com/company/wearedevelopers.org/
Views: 419 WeAreDevelopers
Text Mining for Social Scientists
 
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Text mining refers to digital social research methods that involve the collection and analysis of unstructured textual data, generally from internet-based sources such as social media and digital archives. In this webinar, Gabe Ignatow and Rada Mihalcea discussed the fundamentals of text mining for social scientists, covering topics including research design, research ethics, Natural Language Processing, the intersection of text mining and text analysis, and tips on teaching text mining to social science students.
Views: 915 SAGE
Lexaytics User Group Conference - Seth Grimes on Text Analytics: Where We Are, Where We're Heading
 
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Seth Grimes of Alta Plana discusses the current state of Text Analytics, and what the future holds in store for us. http://www.lexalytics.com http://www.altaplana.com
Views: 151 Lexalytics
Text mining Lecture 7
 
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Text Mining Lecture 7 Topic: Natural Language Processing in Accounting, Auditing and Finance: A synthesis of the Literature with a Roadmap for Future Research 01:33 Major Contribution of the Paper 02:56 Introduction 03:47 Objective 04:17 Literature Selection & Assessment 08:43 Analysis of Sample size N 14:11 NLP in Accounting , Auditing and Finance 16:48 Knowledge Organization, Categorization, and Retrieval 17:49 Taxonomy & Thesauri Generation 18:30 Information Retrieval 20:23 Fraud Prediction and Detection 21:57 Predicting Stock Prices and Market Activity 23:36 Firm- Specific Predicitions 24:23 Predictive Value of Annual Reports and Disclosures 25:27 Predictive of Web Content 29:56 Natural Language Processing & Readability Studies Topic: Detecting deceptive discussion in conference calls 36:29 Motivation 38:47 Literature review on linguistic features 44:29 Development of word lists to measure deception 1:02:53 Data 1:04:30 Parsing method for conference calls 1:10:29 Results for CFO 1:13:01 Similarities in Linguistic cues 1:15:01 Coding 1:23:02 Software Repository for Accounting and Finance
Directed Text Mining - Russ Stephenson - Part 1 of 2
 
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The presentation -Directed Text Mining- by Russ Stephenson given during the MapWindow Conference 2011 in San Diego, USA
Views: 108 MapWindow.nl
Reinhard Altenhöner - Access to knowledge: Text mining and information extraction
 
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“From Big Data to Smart Knowledge – Text and Data Mining in Science and Economy”, Conference in Cologne February 23 to 24 2015 www.textminingconference.de
Large scale Biomedical text mining using machine learning
 
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In this YouTube video I am briefly describing in less than 20 minutes the great potential benefits of using Map Reduce Paradigm in Natural Language Processing especially for biomedical text mining and personalized medicine at the MCBIOS bioinformatics conference hosted by the Oklahoma State University, in Stillwater, Oklahoma held from March 6th-8th 2014. For any questions, comments, suggestions please contact: Thomas Hahn Email: [email protected] Skype ID: tfh002 Cell phone: 318 243 3940 Office phone: 501 682 1440 Office Location: EIT 535 (Graduate student in the joined bioinformatics program at the University of Arkansas at Little Rock (UALR) and the University of Arkansas Medical Science (UAMS)) and/or Prasanna Balakrishnan Flat no 22 Home Finders Court Chromepet Chennai - 600 044 email: [email protected] phone no - +91 9444708436
Views: 420 Thomas Hahn
Luís Paulo Reis - Text Mining to Improve Qualitative Data Analysis of Large Document Collections
 
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Closing Conference // 3rd World Conference on Qualitative Research (17 to 19 October 2018 - Lisbon, Portugal) Text Mining to Improve Qualitative Data Analysis of Large Document Collections Text Mining is the process of deriving high-quality information from text using analytical and natural language processing methods. Text analysis involves among others, information retrieval, lexical analysis, pattern recognition, tagging/annotation, information extraction, data mining, visualization, and predictive analytics. The main goal is, essentially, to turn text into valid information for analysis. This Talk introduces the main processes and methodologies of text mining to support qualitative data analysis of large-scale document collections. It aims to contribute to the steadily growing field of qualitative research facing the challenge to consolidate text analysis methods to be able to process vast amounts of text, in a semi-autonomous manner, using modern data and text mining algorithms. The talk illustrates with examples and results from recent LIACC projects on this area with emphasis on our projects on complaints analysis developed together with the Portuguese Government (Min. Economy/ASAE) and several projects developed together with large companies such as Twitómetro/TwitterEcho, VOXX, Time Machine, Financial Sentiment Analysis and Argumentation Mining.
Prof. Lars Juhl Jensen - Pragmatic text mining: From literature to electronic health records
 
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“From Big Data to Smart Knowledge – Text and Data Mining in Science and Economy”, Conference in Cologne February 23 to 24 2015 www.textminingconference.de
Explainable NLP Algorithms: Understanding Word Relevance in Text Datasets
 
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Speaker: Pramit Choudhary, DataScience Presented on November 30, 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).
Using twitter to predict heart disease | Lyle Ungar | TEDxPenn
 
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Can Twitter predict heart disease? Day in and day out, we use social media, making it the center of our social lives, work lives, and private lives. Lyle Ungar reveals how our behavior on social media actually reflects aspects about our health and happiness. Lyle Ungar is a professor of Computer and Information Science and Psychology at the University of Pennsylvania and has analyzed 148 million tweets from more than 1,300 counties that represent 88 percent of the U.S. population. His published research has been focused around the area of text mining. He has published over 200 articles and holds eleven patents. His current research deals with statistical natural language processing, spectral methods, and the use of social media to understand the psychology of individuals and communities. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 3831 TEDx Talks
What are the current challenges in Text and Data Mining? - Monica Ihli
 
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Video recorded at the Workshop On mining Scientific Publications, 19th-23rd June at The University of Toronto, as a part of JCDL 2017 (Joint Conference on Digital Libraries).
Views: 99 OpenMinTeD
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).
What are the benefits of Text and Data Mining? - Monica Ihli
 
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Video recorded at the Workshop On mining Scientific Publications, 19th-23rd June at The University of Toronto, as a part of JCDL 2017 (Joint Conference on Digital Libraries).
Views: 170 OpenMinTeD
Jakob Zeitler, Travis Coan, "Text-Mining the Signals of Climate Change Doubt"
 
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Jakob Zeitler, Travis Coan "Text-Mining the Signals of Climate Change Doubt" University of Exeter CompSust-2016 Computational Sustainability conference July 7, 3:30 PM at Cornell University​ Lightning talk in the Machine and Statistical Learning for Conservation, Poverty, Energy, and Climate session
Views: 189 CompSustNet
Tricks, tips and topics in Text Analysis - Bhargav Srinivasa Desikan
 
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PyData Amsterdam 2018 There is an abundance of easily mineable text data (Whatsapp, twitter, and even our own e-mails!), and we have no excuse to not analyse it. In this workshop, we will learn some tips and tricks to deal with messy text data, before moving on to some lesser looked at text analysis techniques, such as text summarisation, working with distance metrics, and an old personal favorite - topic models. Slides: https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial -- 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: 1070 PyData
Semantria + Tableau for visual text mining
 
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Using Tableau to visualize text content processed through the Lexalytics Semantria Excel plugin.
Views: 1761 Lexalytics
A Fun Introduction to Text and Data Mining - Federico Nanni
 
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Video recorded at the Workshop On mining Scientific Publications, 19th-23rd June at The University of Toronto, as a part of JCDL 2017 (Joint Conference on Digital Libraries).
Views: 132 OpenMinTeD
Jason Kessler - Using Scattertext and the Python NLP Ecosystem for Text Visualization
 
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Description Scattertext is a Python package that lets you compare and contrast how words are used differently in two types of documents, producing interactive, Javascript-based visualizations that can easily be embedded into Jupyter Notebooks. Using spaCy and Empath, Scattertext can also show how emotional states and words relating to a particular topic differ. Abstract Notebooks and presentation for this talk are available from https://github.com/JasonKessler/Scattertext-PyData. Motivation and introduction -What's the matter with word clouds? -How to read a plot made by Scattertext How to make your own plots -Preparing a Pandas data frame with your data set -Plotting with Scattertext, and fine tuning plots for interpretability and speed Scattertext and the Python NLP ecosystem -Visualizing emotions using Empath. -Using word vectors from spaCy and elsewhere see how topic-specific language differs. -Visualizing topic models from scikit-learn. Links -Source code for the package is hosted on Github at github.com/JasonKessler/scattertext. -For more information, please see the paper which will appear as a 2017 ACL Demo at https://arxiv.org/abs/1703.00565. 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: 3579 PyData
Analyse de données, Data Mining et Big Data
 
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Au delà de la statistique inférentielle de base, par Yves Gueniffey, Maître de Conférences à Mines Nancy.
Views: 57450 Verdel Thierry
Great Lakes Analytics in Sports Conference :: Uwe Neuhaus and Michael Schulz
 
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Live Measurement of Player Performance During Soccer Matches -- A Text Mining Approach During a professional soccer match, a large number of key figures (e.g., shots on goals, fouls) is collected to assess the performance of individual players. However, depending on the player’s position, these figures are not always informative. Expert evaluations are better suited for individual assessment, but are often merely rough summaries and only available after the end of the match. For this reason, we introduce a method to analyze professional live soccer commentary using text mining techniques. The analysis result is a key figure that can be computed in near real time during the match to provide information about the performance of players and, in aggregated form, the team. We evaluate the suitability of our indicator with data from the Confederation Cup 2017. Michael Schulz is Professor of Information Systems at the university of applied sciences NORDAKADEMIE, Hochschule der Wirtschaft, in Elmshorn, Germany. He earned his doctoral degree in business administration from Philipps University of Marburg. His area of expertise is databases and analytical information systems. His research interests include data mining, data modeling and self-service business intelligence. He has extensive experience in consulting large organizations in data warehousing and business intelligence. Uwe Neuhaus is lecturer of computer science and research associate at the university of applied sciences NORDAKADEMIE, Hochschule der Wirtschaft, in Elmshorn, Germany. He studied computer science at the Technical University of Braunschweig. His area of work comprises the design and analysis of algorithms, data science, and software development. His research interests include the application of machine learning algorithms and text analytics.
Views: 14 UWStevensPointCOLS
Directed Text Mining - Russ Stephenson - Part 2 of 2
 
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The presentation -Directed Text Mining- by Russ Stephenson given during the MapWindow Conference 2011 in San Diego, USA
Views: 41 MapWindow.nl
Quantitative Text Analysis for Social Scientists  A talk by Nicole Rae Baerg
 
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Nicole Rae Baerg, lecturer at the Department of Government at the University of Essex, discusses qualitative text analysis in this SAGETalks webinar. Text analysis has a long history in the social sciences and has been commonly used to analyze media coverage. Historically, it involved the human coding of text and this has inherent issues. The digital age has made huge amounts of data available for analysis in the form of newspapers, blogs, social media feeds, government documents, the list goes on! As the technology to automate the analysis and coding of texts has become more available we are able to go beyond this and treat text as quantifiable data. Watch this webinar to learn about the role that quantitative text analysis plays for social scientists when working with such vast amounts of data, as well hearing about ‘QTA in action’ and how social scientists are using text analysis for their research.
Views: 458 SAGE
Elsevier's Gemma Hersh talks text and data mining in Welcoming the Robots plenary at #alpsp14
 
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Gemma Hersh, Policy Director at Elsevier talked about their text and data mining policy at the ALPSP International Conference 2014. She covered why a policy and why this policy in particular, criticisms and response, researcher feedback and UK copyright exception and Europe.
Views: 136 ALPSP
Drug Repurposing for Rare Diseases Conference - Lightning Talk 5
 
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To celebrate Rare Disease Day 2016, Findacure organised its annual scientific conference to create a forum that brings together patient groups, clinicians, researchers, biotech companies, and the pharmaceutical industry to discuss the role that drug repurposing can play in the future of rare disease treatment. As part of the conference, there was a section inviting delegates to share 5 minute lightning talks of their work on drug repurposing. In this talk, Jane Reed from Linguamatics describes the company's text mining solution, which has been used by researchers at Shire Pharmaceuticals to develop insights into the association of genetic variation with severity phenotypes, in Hunter Syndrome patients. If you would like to support the work of Findacure, why not donate to the charity via http://uk.virginmoneygiving.com/charities/findacure
Views: 93 Findacure
Mining for Development Conference 2015
 
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Welcome to the Mining for Development Conference 2015 The Conference will celebrate participants’ goals and achievements in creating effective partnerships, enabling environments, and practical ‘on-the-ground’ solutions to deliver long-term inclusive benefits from mining to developing countries and their communities. A highlight of the Mining for Development Alumni Forum and Conference 2015 will be the coming together of government ministers, mining executives, international agencies and leaders of mining and development policy across the developing world to share their knowledge and experiences. It will provide a platform to engage with key influencers and world leaders to address the theme of the Conference and Alumni Forum: Shared goals – realising benefits. Visit m4dconference.im4dc.org for more details. Hashtag: #M4D2015 #IM4DC
Views: 7246 MEfDA and IM4DC
Text Analytics with R | How to Scrap Website Data for Text Analytics | Web Scrapping in R
 
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In this text analytics with R tutorial, I have talked about how you can scrap website data in R for doing the text analytics. This can automate the process of web analytics so that you are able to see when the new info is coming, you just run the R code and your analytics will be ready. Web scrapping in R is done by using the rvest package. Text analytics with R,how to scrap website data in R,web scraping in R,R web scraping,learn web scraping in R,how to get website data in R,how to fetch web data in R,web scraping with R,web scraping in R tutorial,web scraping in R analytics,web scraping in r rvest,web scraping and r,web scraping regex,web scraping facebook in r,r web scraping rvest,web scraping in R,web scraper with r,web scraping in r pdf,web scraping avec and r,web scraping and r
Text mining online data with scikit-learn by Robert Layton
 
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Text mining has a large variety of applications and is becoming used in more businesses for gathering intelligence and providing insight. People are sending text constantly online via social media, chat rooms and blogs. Tapping into this information can help businesses gain an advantage and is increasingly a necessary skill for data analytics. Text mining is a unique data mining problem, dealing with real world data that is often heavy on artefacts, difficult to model and challenging to properly manage. Text mining can be seen as a bit of a dark art that is difficult to learn and gain traction. However some basic strategies can often be applied to get good results quite quickly, and the same basic models appear in many text mining challenges. The scikit-learn project is a library of machine learning algorithms for the scientific python stack (numpy & scipy). It is known for having detailed documentation, a high quality of coding and a growing list of users worldwide. The documentation includes tutorials for learning machine learning as well as the library and is a great place to start for beginners wanting to learn data analytics. There is a strong focus on reusable components and useful algorithms, and the text mining sections of scikit-learn follow the “standard model” of text mining quite well. In this presentation, we will go through the scikit-learn project for machine learning and show how to use it for text mining applications. Real world data and applications will be used, including spam detection on Twitter, predicting the author of a program and determining a user's political bent based on their social media account. PyCon Australia is the national conference for users of the Python Programming Language. In August 2014, we're heading to Brisbane to bring together students, enthusiasts, and professionals with a love of Python from around Australia, and all around the World. August 1-5, Brisbane, Queensland, Australia
Views: 4227 PyCon Australia
Eulalia Veny - Recipe for text analysis in social media
 
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Recipe for text analysis in social media: [EuroPython 2018 - Talk - 2018-07-25 - PyCharm [PyData]] [Edinburgh, UK] By Eulalia Veny The analysis of text data in social media is gaining more and more importance every day. The need for companies to know what people think and want is key to invest money in providing customers what they want. The first approach to text analysis was mainly statistical, but adding linguistic information has been proven to work well for improving the results. One of the problems that you need to address when analyzing social media is time. People are constantly exchanging information, users write comments every day about what they think of a product, what they do or the places they visit. It is difficult to keep track of everything that happens. Moreover, information is sometimes expressed in short sentences, keywords, or isolated ideas, such as in Tweets. Language is usually unstructured because it is composed of isolated ideas, or without context. I will talk about the problem of text analysis in social media. I will also explain briefly Naïve Bayes classifiers, and how you can easily take advantage of them to analyse sentiment in social media, and I will use an example to show how linguistic information can help improve the results. I will also evaluate the pros and cons of supervised vs unsupervised learning. Finally, I will introduce opinion lexicons, both dictionary based and corpus-based, and how lexicons can be used in semi-supervised learning and supervised learning. If I have time left, I will explain about other use cases of text analysis. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2018.europython.eu/en/speaker-release-agreement/
Dominik Slezak  |  Poland | Big Data Analysis and Data Mining  2015 | Conference Series LLC
 
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2nd International Conference on Big Data Analysis and Data Mining November 30-December 01, 2015 San Antonio, USA Scientific Talk On: Knowledge Pit Platform for Modern Data Mining Competitions Click here for Abstract and Biography: http://datamining.conferenceseries.com/speaker/2015/dominik-slezak-university-of-warsaw-poland Conferenceseries LLC : http://www.conferenceseries.com Omics International : http://www.omicsonline.org/
Process mining - the next big thing - 9th Annual ABSL Conference
 
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9th Annual ABSL Conference Day 2, 08.06.2018 "Process mining - the next big thing" Rasto Hlavac - CEO Minit
Views: 64 ABSL Polska
New Wine - Hillsong Worship
 
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'New Wine' from our new album, "There Is More". Order the album: http://hllsng.co/thereismore?IQid=youtube.descriptions Subscribe to our YouTube channel: http://smarturl.it/HillsongWorshipSub Stay connected: http://www.facebook.com/hillsongworship http://twitter.com/hillsongworship http://instagram.com/hillsongworship http://hillsong.com/worship VERSE 1: In the crushing In the pressing You are making new wine In the soil I now surrender You are breaking new ground PRE-CHORUS: So I yield to You and to Your careful hand When I trust You I don’t need to understand CHORUS: Make me Your vessel Make me an offering Make me whatever You want me to be I came here with nothing But all You have given me Jesus bring new wine out of me VERSE 2: In the crushing In the pressing You are making new wine In the soil I now surrender You are breaking new ground VERSE TAG: You are breaking new ground BRIDGE: Where there is new wine There is new power There is new freedom The Kingdom is here I lay down my old flames To carry Your new fire today Words and Music by Brooke Ligertwood © 2017 Hillsong Music Publishing CCLI: 7102397
Views: 16117366 Hillsong Worship
Text Mining
 
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Views: 17 fruk kap
"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: 181 Six Feet Up Corp

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