This is a example in GATE which shows the results of the default ANNIE pipeline on an English document. In this case the document is "That's what she said" that lovely catch phrase from Michael Scott in The Office TV show http://www.cs.washington.edu/homes/brun/pubs/pubs/Kiddon11.pdf it discusses humor recognition...
Views: 30287 cesine0
This is the part tWO tutorial showing how you can perform basic text mining with General architecture for text engineering (GATE) using the JAPE transducer
Views: 294 ChudeTV
Tiger Zhang & Lutz Finger on Text Mining Today more than ever before, we have access raw data in the form of texts. Businesses around the world store text discussions from their market research, customer care discussions, or brand relevant conversation on social media. While it is clear that texts contain valuable information, it is often less clear on how best texts can be analyzed at scale. In this class, we will share how we at LinkedIn built a scalable text-mining platform to uncover insights from text data. We will focus on two important components: THEME DISCOVERY of new content and how to CLASSIFY existing text. Using both features, we can detect emerging trends within reviews, customer care discussions and market research data. You will learn: THEME DISCOVERY - information extraction Theme recognition is a highly complex task due to the multi-facetted nature of our language. Theme Recognition (without requiring manual reviews) is, however, the main component of any text-mining platform. We will introduce an innovation in information extraction using part of speech tagging (currently patent pending) to uncover themes within textual data. TEXT CLASSIFICATIONS - Supervised Machine Learning Another important component of our NLP platform is the ability to classify text via supervised machine learning algorithms such as support vector machine (SVM). The ability to classify serves many business use-cases ranging from sentiment analytics to product identification. You will learn in our talk how to cater to those different requirements via a flexible platform setup. VALUE of DATA - Member Feedback The combined ability of Themes Discovery (new content and ideas) as well as Classifications (standard measure) creates a very effective framework to get business insights out of text data. We will demonstrate this on the use case of classifying and responding to member feedback.
Views: 12767 Lutz Finger
WordStat a content analysis and text mining software from Provalis Research.
Views: 14418 Provalis Research - Text Analytics Software
This example takes a Course syllabus (mostly semantics courses) and highlights the reading lists using Jape grammars. It recognizes things like Van Fintel and Heim 2003 as a citation and Chapters 1, 3 and 8 as a reading selections and Week 1 as a due date (among others). Its another example of what GATE can do, in this case to help automate tasks like downloading a reading list. The files are in here https://github.com/cesine/GATEinSpring/tree/master/gate/WEB-INF/gate-files
Views: 12706 cesine0
Think of this as an unboxing video for annotation software - this is the first time I've tried running any of this software. Don't expect any good demos, I'm just showing you where to find them along with some resources. GATE https://gate.ac.uk/family/ MAE2 https://keighrim.github.io/mae-annotation/ BRAT http://brat.nlplab.org/features.html WebAnno https://webanno.github.io/webanno/ Annis http://corpus-tools.org/annis/ SLATE https://bitbucket.org/dainkaplan/slate/ Works cited: Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications https://smile.amazon.com/Natural-Language-Annotation-Machine-Learning/dp/1449306667/ Overview of Annotation Creation: Processes & Tools. Finlayson, Mark & Erjavec, Tomaž. (2016). https://www.researchgate.net/publication/301847215_Overview_of_Annotation_Creation_Processes_Tools Handbook of Linguistic Annotation. "Collaborative Web-Based Tools for Multi-layer Text Annotation" pp 229-256 https://link.springer.com/chapter/10.1007/978-94-024-0881-2_8 Also, this is the document I meant to show at 14:21 in the video: Annotation Process Management Revisited Dain Kaplan, Ryu Iida, Takenobu Tokunaga Department of Computer Science, Tokyo Institute of Technology http://www.lrec-conf.org/proceedings/lrec2010/pdf/129_Paper.pdf
Views: 1732 Norman Gilmore
In less than 5 minutes, we take 20,000 tweets from Datasift, perform text mining through the Lexalytics/Semantria Excel Plugin, import the results into Tableau, and start visualizing cool stuff. (This process applies to Tableau version 8.2).
Views: 19189 Lexalytics
In this introduction to text mining with Voyant I cover: 1) Data cleaning (text editors, Notepad++ and Sublime Text) 2) Loading your text into Voyant 3) Expectations, what Voyant can and cannot do 4) Working with common visualization tools and making possible connections 5) Exporting visualizations
Views: 313 Bruce Matsunaga - ASU
Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&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: 100504 Siraj Raval
In this tutorial, we will go over how to utilize LIWC software (http://liwc.wpengine.com/) to conduct content and sentiment analysis on your very own documents. This is Part 2/2 of our video series showing how to scrape and analyze reddit comment threads. For Part 1, follow the link: https://www.youtube.com/watch?v=yexxcrPC7U8&feature=youtu.be
Views: 6628 I Johar
A short introduction session on how to download, install and get going with the one month free trial of Quirkos - easy to use software for qualitative text data analysis.
Views: 193 Quirkos Software
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: 22 davidwilsonlibrary
In this video I process transcriptions from Hugo Chavez's TV programme "Alo Presidente" to find patterns in his speech. Watching this video you will learn how to: -Download several documents at once from a webpage using a Firefox plugin. - Batch convert pdf files to text using a very simple script and a java application. - Process documents with Rapid Miner using their association rules feature to find patterns in them.
Views: 35740 Alba Madriz
This video is a project to corroborate on whether movie plot summary would help in predicting a movie's box office success. Plot summary of a movie from any website is taken and is text processed to generate word vectors. Then a prediction model is developed which trains this data and applies the model to the testing data.
Views: 1247 dinesh yadav
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Using CART and logistic regression to predict negative sentiment. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Views: 2558 MIT OpenCourseWare
An overview of how I2E can be used to extract chemical structures in context within literature. Using a combination of Natural Language Processing (NLP) and JChem, I2E can identify a wide range of chemicals within text and provide an insight into the context within which they are being referred to.
Views: 433 ChemAxon
Biohunter is a single portal which provides literature search, data statistics, reading, sorting, storing, field expert identification and journal finder for your innovative research.
Views: 1265 Exonn Technologies
An Ontology Based Text Mining Framework for R&D Project Selection ieee project in java
Views: 361 satya narayana