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Build a Text Summarizer in Java
 
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Get the Code here : https://github.com/ajhalthor/text-summarizer Follow me on Twitter : https://twitter.com/ajhalthor Take a look at the original by Shlomi Babluki : http://thetokenizer.com/2013/04/28/build-your-own-summary-tool/ TRANSCRIPT OVERVIEW ALGORITHM 1. Take the full CONTENT and split it into PARAGRAPHS. 2. Split each PARAGRAPH into SENTENCES. 3. Compare every sentence with every other. This is done by Counting the number of common words and then Normalize this by dividing by average number of words per sentence. 4. These intermediate scores/values are stored in an INTERSECTION matrix 5. Create the key-value dictionary - Key : Sentence - Value : Sum of intersection values with this sentence 6. From every paragraph, extract the sentences with the highest score. 7. Sort the selected sentences in order of appearance in the original text to preserve content and meaning. And like that, you have generated a summary of the original text. CLASSES IN JAVA PROJECT 1. Sentence : The entire text is divided into a number of paragraphs and each paragraph is divided into a number of sentences. 2. Paragraph : Every paragraph has a number associated with it and an Array List of sentences. 3. Sentence Comparitor : Compare Sentence objects based on Score 4. SentenceComparatorForSummary : Compare Sentence objects based on position in text. 5. SummayTool : akes care of all the operations from extracting sentences to generating the summary. HOW IS MY SUMMARIZER BETTER THAN THE ORIGINAL ? My text summarizer selects number of sentences from a paragraph depending on the length. This is an improvement over the original text summarizer implementation that only selects 1 sentence per paragraph regardless of length. So, If the author decides to crunch everything into 1 paragraph, then only one sentence would be chosen. In the current implementation, we set it to accept several sentences for larger paragraphs. It delivers cogent summaries for general essays, reviews and publications. RUN THIS PROGRAM $ javac -d bin improved_summary.java $ java -classpath bin improved_summary
Views: 5131 CodeEmporium
#32 Text Analysis using Java Program || Core Java in Tamil
 
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This video guides you through step by step instructions on how to build a class based java program to accomplish the following specification: Write a loop that will examine each character in a string called "text" and determine how many of the characters are letters, how many are digits, how many are white space characters, and how many are other kinds of characters (eg. punctuation characters). (in Tamil) This Video is Part of “Professional Degree in Core Java in Tamil” You can Watch all videos, click this link : https://goo.gl/g3Tz6r For a full list of our YouTube courses, visit our website: http://cka.collectiva.in/programming Contact Details : Feel free to Call : (+91) 850 850 2000 By Collectiva Knowledge Acadamy http://cka.collectiva.in Related searches: java in tamil, java in tamil tutorial, java in tamil language, java programming in tamil, java programming tutorial in tamil, learn java in tamil, learn core java in tamil, learn java programming for beginners in tamil,Text Analysis using Java Program
Naive Bayes w/ JAVA - Tutorial 01
 
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Website + download source code @ http://www.zaneacademy.com
Views: 3074 zaneacademy
Java Tutorials - Data mining - part 1/3
 
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How to mine data from a website? How to programatically submit forms? How to process the output? How to keep session? How to work with cookies? I'm trying to answer all of these in this three part tutorial. PARTS: Java Tutorials - Data mining - part 1/3 https://www.youtube.com/watch?v=Lm9iDtQJAxM Java Tutorials - Data mining - part 2/3 https://www.youtube.com/watch?v=mlmgNWKCevE Java Tutorials - Data mining - part 3/3 https://www.youtube.com/watch?v=a-1SggM9ci8
Views: 12051 Leny the serf
Getting Started with Natural Language Processing in Java : Simple Java Tokenizers | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2xhnAS7]. The aim of this video is to demonstrate core Java tokenizers. • Learn to use the Scanner class to tokenize text • Learn how to use the BreakIterator class for tokenization • Learn how to use the StringTokenizer For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 3444 Packt Video
Weka classifier from Java
 
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Final proyect, using classifier on diabetes dataset. Authors: Oyervide Jonnathan & Poveda Adrian
Views: 3675 Adrian Poveda
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: 14268 The Coding Train
Java Implementation of K-Nearest Neighbors (kNN) Classifier 1/2
 
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The code can be found here: www.imperial.ac.uk/people/n.sadawi Go to Tutorials and then Machine Learning section!
Views: 31032 Noureddin Sadawi
Final Year Projects | Effective Pattern Discovery for Text Mining | ClickMyProject
 
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Effective Pattern Discovery for Text Mining -Final Year Projects More Details: Visit http://clickmyproject.com/effective-pattern-discovery-for-text-mining-p-116.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us : [email protected]
Views: 5891 ClickMyProject
Kmeans Clustering Solved Example with Java Code
 
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Link for example file: https://drive.google.com/open?id=0B8CebiqB_IUoQ1JwWV92WVY5Ync Link for Java Code: https://drive.google.com/open?id=0B8CebiqB_IUoUmFTWmN5TFNqbE0 If you find any problem do comment below i ll help you out.
Views: 7496 AVINASH YADAV
Formatting Text Output (Java)
 
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This program shows you how to read text from a file, process it, and output values in a formatted table.
Views: 1662 Such Code
Cosine Similarity and IDF Modified Cosine Similarity
 
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This video tutorial explains the cosine similarity and IDF-Modified cosine similarity with very simple examples (related to Text-Mining/IR/NLP). It also demonstrates the Java implementation of cosine similarity. The source code can be downloaded from:- 1. Cosine similarity: https://sites.google.com/site/nirajatweb/home/technical_and_coding_stuff/cosine_similarity 2. IDF-Modified cosine similarity: https://sites.google.com/site/nirajatweb/home/technical_and_coding_stuff/idf_modified_cosine_similarity
Views: 7682 Dr. Niraj Kumar
KNN Classifier Java
 
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Data Mining Coursework 1 - KNN Classifier in Java
Views: 591 Leo Petrokofsky
Effective Pattern Discovery for Text Mining 2012 IEEE JAVA
 
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Effective Pattern Discovery for Text Mining 2012 IEEE JAVA TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.
Views: 3726 jpinfotechprojects
What is Text Mining?
 
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An introduction to the basics of text and data mining. To learn more about text mining, view the video "How does Text Mining Work?" here: https://youtu.be/xxqrIZyKKuk
Views: 38429 Elsevier
BoilerPipe - quick HTML full text extraction
 
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video demonstrates how you can use the open source BoilerPipe library to extract text from a HTML. The various extractors provided in the library handle removing the pages boiler plate HTML (ie removes the header/footer etc) so that you can focus on processing the main text on the page.
Views: 2289 Melvin L
PDFBox Example Code: How to Extract Text From PDF file with java
 
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For those who want to learn full video series on PDFBox , checkout my full tutorial series on it here : https://goo.gl/xORFiL Post Link : http://radixcode.com/pdfbox-example-code-how-to-extract-text-from-pdf-file-with-java/
Views: 39816 Radix Code
How to get data from twitter using java
 
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Helps you to get data from twitter using twitterAPI
Views: 43292 Atik khan
Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research
 
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Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 3272 ClickMyProject
Calling Java code from R using the rJava package
 
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We show how to design a class in Java, and import it in R. Thus, we can encapsulate our R code and functions into consistent classes
Views: 1414 Datax Jobs
Genetic Algorithms Tutorial 06 - data mining + JAVA 8 + logical operators
 
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Website + download source code @ http://www.zaneacademy.com
Views: 1715 zaneacademy
Web Scraping with Java(Extract Website Data): The EASY Way
 
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In this video we will see how to fetch data from a website using java. This is also known as Web Scraping. We are going to use Jsoup for this purpose. If you like the video Please subscribe to our channel and hit the like button: Subcribe : https://www.youtube.com/channel/UCF-RAxRXVC1o_30flQ-hy7w?sub_confirmation=1 Libraries Required Jsoup : https://jsoup.org/download
Views: 20517 GadgetsByBG
Real time Twitter Opinion Mining and Tweet Clustering in Java ( Netbeans)
 
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This work uses SentiStrength Database with an improved algorithm for sentiment analysis in tweets. It also uses advanced pattern matching techniques with automata, weight enhancement of senti words based on preceding terms like "very" "Nice". It reverses the polarity based on negative words before senti words. Not Good is considered as bad. Refer my paper for more details: http://www.ijera.com/papers/Vol2_issue1/BM021412416.pdf
Views: 5431 rupam rupam
Java: Read text file efficiently with BufferedReader
 
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Read a text file with standard Java BufferedReader. This is faster than RandomAccessFile, but slightly more lines of code. Demo of BufferedReader and FileReader. We process the text file line by line and then close the text file. Good beginning programming example. Follow along!
Views: 3227 cbttjm
Textmining using indexing Java project
 
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ePRoSOFT Technologies,Kharghar-Navi Mumbai. 02266730140/9321060440/9769890003 [email protected]/[email protected] 5 min. walk from Kharghar station.
Views: 1993 ePRoSOFToProjects
Text Classification using Spark Machine Learning
 
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The goal of text classification is the classification of text documents into a fixed number of predefined categories. Text classification has a number of applications ranging from email spam detection to providing news feed content to users based on user preferences. In this session, we explore how to perform text classification using Spark’s Machine Learning Library (MLlib). We see how MLlib provides a set of high-level APIs for constructing, evaluating and tuning a machine learning workflow. We explore how Spark represents a workflow as a Pipeline, which consists of a sequence of stages to be run in a specific order. The Pipeline for our text classification use case utilizes Transformer stages to prepare the raw text documents for classification, and Estimator stages to learn a machine learning model that can be used to classify documents. Finally, we illustrate how to tune the model for best fit. Although a document classification use case is specifically explored, many of the principles demonstrated in the session can be employed in a variety of other machine learning use cases. Here's the link to the slides https://ibm.box.com/s/atp4ezwvo5jr27zpxlu4987ercep2arn And the link to the notebook as an .ipynb file. https://ibm.box.com/s/spcj7f3uz6qetq8442mnvw5j264wbilj
Views: 9605 Data Gurus
Java Reading a CSV File Tutorial
 
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We show how to read and parse a .csv text file, using Scanner.
Views: 100505 José Vidal
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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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: 154398 Timothy DAuria
Text Classification Using Naive Bayes
 
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This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 79210 Francisco Iacobelli
Naive Bayes  w/ JAVA (Tutorial 02) - Sentiment Classification + Laplace Smoothing + Handle Underflow
 
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Website + download source code @ http://www.zaneacademy.com
Views: 1359 zaneacademy
An Ontology Based Text Mining Framework for R&D Project Selection
 
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An Ontology Based Text Mining Framework for R&D Project Selection ieee project in java
Views: 334 satya narayana
Text Mining the Contributors to Rail Accidents||ieee 2017 java projects at bangalore
 
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We are providing a Final year IEEE project solution & Implementation with in short time. If anyone need a Details Please Contact us Mail: [email protected] or [email protected] Phone: 09842339884, 09688177392 Watch this also: https://www.youtube.com/channel/UCDv0caOoT8VJjnrb4WC22aw ieee projects, ieee java projects , ieee dotnet projects, ieee android projects, ieee matlab projects, ieee embedded projects,ieee robotics projects,ieee ece projects, ieee power electronics projects, ieee mtech projects, ieee btech projects, ieee be projects,ieee cse projects, ieee eee projects,ieee it projects, ieee mech projects ,ieee e&I projects, ieee IC projects, ieee VLSI projects, ieee front end projects, ieee back end projects , ieee cloud computing projects, ieee system and circuits projects, ieee data mining projects, ieee image processing projects, ieee matlab projects, ieee simulink projects, matlab projects, vlsi project, PHD projects,ieee latest MTECH title list,ieee eee title list,ieee download papers,ieee latest idea,ieee papers,ieee recent papers,ieee latest BE projects,ieee B tech projects| Engineering Project Consultants bangalore, Engineering projects jobs Bangalore, Academic Project Guidance for Electronics, Free Synopsis, Latest project synopsiss ,recent ieee projects ,recent engineering projects ,innovative projects| Computer Software Project Management Consultants, Project Consultants For Electrical, Project Report Science, Project Consultants For Computer, ME Project Education Consultants, Computer Programming Consultants, Project Consultants For Bsc, Computer Consultants, Mechanical Consultants, BCA live projects institutes in Bangalore, B.Tech live projects institutes in Bangalore,MCA Live Final Year Projects Institutes in Bangalore,M.Tech Final Year Projects Institutes in Bangalore,B.E Final Year Projects Institutes in Bangalore , M.E Final Year Projects Institutes in Bangalore,Live Projects,Academic Projects, IEEE Projects, Final year Diploma, B.E, M.Tech,M.S BCA, MCA Do it yourself projects, project assistance with project report and PPT, Real time projects, Academic project guidance Bengaluru| Image Processing ieee projects with source code,VLSI projects source code,ieee online projects.best projects center in Chennai, best projects center in trichy, best projects center in bangalore,ieee abstract, project source code, documentation ,ppt ,UML Diagrams,Online Demo and Training Sessions|Rail safety, safety engineering, latent Dirichlet allocation, partial least squares, random forests|PLS predictor|RMSE from cross-validation with different numbers of components.
Views: 104 SD Pro Solutions
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 4810 ClickMyProject
Knuth–Morris–Pratt(KMP) Pattern Matching(Substring search)
 
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Pattern matching(substring search) using KMP algorithm https://www.facebook.com/tusharroy25 https://github.com/mission-peace/interview/blob/master/src/com/interview/string/SubstringSearch.java https://github.com/mission-peace/interview/wiki
Getting Started with Natural Lang Processing in Java : Extracting Text from Web Page | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2xhnAS7]. The aim of this video is to demonstrate how text can be extracted from a web page. • Learn how to extract basic information from a web page • Extract text from the body of a web page • Access links and images found in a web page For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 101 Packt Video
Final Year Projects | Mining Social Emotions from Affective Text
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 652 ClickMyProject
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
 
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Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 37916 DeepLearning.TV
Sentiment Analysis in 4 Minutes
 
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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/
Views: 78580 Siraj Raval
Regular Expressions (Regex) Tutorial: How to Match Any Pattern of Text
 
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In this regular expressions (regex) tutorial, we're going to be learning how to match patterns of text. Regular expressions are extremely useful for matching common patterns of text such as email addresses, phone numbers, URLs, etc. Almost every programming language has a regular expression library, so learning regular expressions with not only help you with finding patterns in your text editors, but also you'll be able to use these programming libraries to search for patterns programmatically as well. Let's get started... The code from this video can be found at: https://github.com/CoreyMSchafer/code_snippets/tree/master/Regular-Expressions Python Regex Tutorial: https://youtu.be/K8L6KVGG-7o If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Tumblr - https://www.tumblr.com/blog/mycms
Views: 100103 Corey Schafer
TEXT MINING PROJECTS IN AUSTRIA
 
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DOTNET PROJECTS,2013 DOTNET PROJECTS,IEEE 2013 PROJECTS,2013 IEEE PROJECTS,IT PROJECTS,ACADEMIC PROJECTS,ENGINEERING PROJECTS,CS PROJECTS,JAVA PROJECTS,APPLICATION PROJECTS,PROJECTS IN MADURAI,M.E PROJECTS,M.TECH PROJECTS,MCA PROJECTS,B.E PROJECTS,IEEE PROJECTS AT MADURAI,IEEE PROJECTS AT CHENNAI,IEEE PROJECTS AT COIMBATORE,PROJECT CENTER AT MADURAI,PROJECT CENTER AT CHENNAI,PROJECT CENTER AT COIMBATORE,BULK IEEE PROJECTS,REAL TIME PROJECTS,RESEARCH AND DEVELOPMENT,INPLANT TRAINING PROJECTS,STIPEND PROJECTS,INDUSTRIAL PROJECTS,MATLAB PROJECTS,JAVA PROJECTS,NS2 PROJECTS, Ph.D WORK,JOURNAL PUBLICATION, M.Phil PROJECTS,THESIS WORK,THESIS WORK FOR CS
Views: 36 ranjith kumar
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 127654 Brandon Weinberg
Java News Crawler and Rapidminer Keyword Analysis Project
 
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This is a project I put together for my Management Information Systems class this semester at Iowa State University. Wrote a java program that checks for new news articles and then used Rapidminer to do a simple keyword analysis of the downloaded articles. Things I forgot to mention in this video (I'm sure there's more): 1. The website the java program is downloading the rss articles is called fulltextrssfeed.com 2. I forgot I made some changes to one of the article methods. That's why you only 3 articles in the "new" folder. I swear works now though... 3. I understand that there are more efficient ways to write the java program. I just went with the simplest/most visual route for faster development.
Views: 5214 wusupjohn
Finding Elements of Text with NLP in Java :Text Classification & Sentiment Analysis|packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2jcNC0a]. The aim if this video is to make you understand the applications of models in text classification • Understand the nature of text classification • Learn about the utility of text classification • Learn about the importance of models in text classification For the latest Big Data and Business Intelligence tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 163 Packt Video
Weka Tutorial 14: The Java API with Eclipse (Application)
 
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In this tutorial I showed how you can download and incorporate the Weka API with Eclipse Java IDE. The download link for the api is http://www.cs.waikato.ac.nz/ml/weka/
Views: 36063 Rushdi Shams
Document Summarization PART-1: Pagerank based document summarization)
 
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This video tutorial explains, graph based document summarization system (developed by using pagerank algorithm). A java implementation of the system is also demonstrated. The supporting code for the entire demonstration is available at: https://sites.google.com/site/nirajatweb/home/technical_and_coding_stuff/textrank-and-lexrank-based-single-document-summarization
Views: 4587 Dr. Niraj Kumar
Genetic Programming in Java with TinyGP (Part 6 - Data Mining)
 
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A continuing series on Riccardo Poli's TinyGP Java program. In this installment, we make a minor modification by refactoring TinyGP with three logic operators in order to allow the program to do some basic data mining of relationships between input values to a target value. A very obvious toy scenario is first introduced, and then a more involved scenario is built, a formula derived, and an analysis done by with a simple spreadsheet.
Views: 422 Brint Montgomery
Weka Tutorial 15: Java API 101 (Application)
 
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In this tutorial, I showed how to interact with the Weka API for the first time with a simple Java code. In this code, I have loaded an ARFF file called 2.arff and then used Naive Bayes classifier with a 10 fold CV setup. I showed the standard output of Weka on the Eclipse output as well as the F-score, precision and recall of the 10 fold CV.
Views: 49465 Rushdi Shams
Weka 3 data mining java tool - Tutorial 01 (download, install, and test run)
 
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http://www.zaneacademy.com | download source code @ http://sites.fastspring.com/zaneacademy/product/all | Download, install, & test run Weka 3 data mining java tool
Views: 13373 zaneacademy