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Search results “Text mining applications in finance”
"Text Mining Unstructured Corporate Filing Data" by Yin Luo
 
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Yin Luo, Vice Chairman at Wolfe Research, LLC presented this talk at QuantCon NYC 2017. In this talk, he showcases how web scraping, distributed cloud computing, NLP, and machine learning techniques can be applied to systematically analyze corporate filings from the EDGAR database. Equipped with his own NLP algorithms, he studies a wide range of models based on corporate filing data: measuring the document tone or sentiment with finance oriented lexicons; investigating the changes in the language structure; computing the proportion of numeric versus textual information, and estimating the word complexity in corporate filings; and lastly, using machine learning algorithms to quantify the informative contents. His NLP-based stock selection signals have strong and consistent performance, with low turnover and slow decay, and is uncorrelated to traditional factors. To learn more about Quantopian, visit http://www.quantopian.com. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 2079 Quantopian
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
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** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 61282 edureka!
Text Analytics for Finance // Amanda Stent, Bloomberg (FirstMark's Data Driven)
 
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Amanda Stent, Researcher at Bloomberg, spoke at Data Driven NYC in February 2018. She talked about how Bloomberg is using text analytics for finance, and how it differs from NLP in other areas. Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark Capital.
Views: 955 Data Driven NYC
APPLICATIONS OF DATA MINING IN BANKING AND FINANCE
 
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APPLICATIONS OF DATA MINING IN BANKING AND FINANCE
Views: 603 Mehar Ahamed
Text Mining lecture 4
 
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Text Mining Lecture 4 Topic: Making Words work: using financial text as a predator of financial events 1:12 Introduction 1:57 Background and literature 2:49 Vector space model 11:08 Methodology 24:00 Data 26:51 Testing Methodology 28:10 Results 31:27 Substitue- Complement test 32:32 Conclusion Topic: Textual Analyses in Accounting and Finance: A Survey 35:38 History of textual analyses 37:20 Background for business Textual Analyses 38:06 Related Literature 39:37 Challenges of textual analyses 41:54 Examples of studies using Readability 44:45 Defining and Measuring Readability 1:02:05 Bag of Words Methods 1:03:17 Word Lists 1:06:49 Zip’s Law 1:07:31 Term Weighting 1:07:58 Naive Bayes Methods 1:09:02 Thematic Structure in Documents 1:09:48 Implementation 1:11:28 Areas for future Research in Textual Analysis 1:12:40 Conclusion 1:13:44 Python Coding Please subscribe to our channel to get the latest updates on the RU Digital Library. To receive additional updates regarding our library please subscribe to our mailing list using the following link: http://rbx.business.rutgers.edu/subsc…
News Sentiment Analysis Using MATLAB and RavenPack
 
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Use MATLAB® to analyze news sentiment with data from RavenPack®, including retrieving historical data and real-time data. Also, create trading rules based on news sentiment score. To Request a trial of Datafeed Toolbox, visit: https://www.mathworks.com/programs/trials/trial_request.html?prodcode=DF&s_iid=main_trial_DF_tb&s_eid=PEP_12669 Datafeed Toolbox™ provides access to current, intraday, historical, and real-time market data from leading financial data providers. By integrating these data feeds into MATLAB®, you can perform analyses, develop models, and create visualizations that reflect current financial and market behaviors. The toolbox also provides functions to export MATLAB data to some data service providers. You can establish connections from MATLAB to retrieve historical data or subscribe to real-time streams from data service providers. With a single function call, the toolbox lets you customize queries to access all or selected fields from multiple securities over a specified time period. You can also retrieve intraday tick data for specified intervals and store it as time-series data. Supported data providers include Bloomberg®, FactSet®, FRED®, Haver Analytics®, Interactive Data™, IQFEED®, Kx Systems®, SIX Financial Information, Thomson Reuters®, and Yahoo!® Finance.
Views: 2520 MATLAB
APPLICATIONS OF DATA MINING
 
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APPLICATIONS OF DATA MINING
Views: 2717 Samuel Hemandro
Text Mining in Publishing
 
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TEXT MINING AND SCHOLARLY PUBLISHING: This short video by John Bond of Riverwinds Consulting discusses Text Mining and the Scholarly Publishing Industry. MORE VIDEOS on TEXT MINING and Scholarly Publishing can be found at: https://www.youtube.com/playlist?list=PLqkE49N6nq3jY125di1g8UDADCMvCY1zk FIND OUT more about John Bond and his publishing consulting practice at www.RiverwindsConsulting.com SEND IDEAS for John to discuss on Publishing Defined. Email him at [email protected] or see http://www.PublishingDefined.com CONNECT Twitter: https://twitter.com/JohnHBond LinkedIn: https://www.linkedin.com/in/johnbondnj Google+: https://plus.google.com/u/0/113338584717955505192 Goodreads: https://www.goodreads.com/user/show/51052703-john-bond YouTube: https://www.youtube.com/c/JohnBond BOOKS by John Bond: The Story of You: http://www.booksbyjohnbond.com/the-story-of-you/about-the-book/ You Can Write and Publish a Book: http://www.booksbyjohnbond.com/you-can-write-and-publish-a-book/about-the-book/ TRANSCRIPT: Hi there. I am John Bond from Riverwinds Consulting and this is Publishing Defined. Today I am going to discuss text mining as it relates to scholarly publishing. Text mining also goes by the phrase text data mining or text analytics. Text mining in scholarly publishing is the process of deriving high-quality information from peer reviewed articles and other content. It does this by processing large amounts of information and looking for patterns within the data, and then evaluating and interpreting the results. Text mining is most beneficial to researchers or other power users of technical content. It is very different from a keyword search such that you might perform with Google. A key word search likely produces thousands of web links with no uniformity in the results and certainly no ability to draw meaningful conclusions. An example: let’s say you are researching bladder cancer in men and you are looking for specific biomarkers for other disease states. You probably don’t have the time to review all the literature you might find through a search at PubMed. Text mining will review the available literature. It understands the parts of speech (nouns, verbs), recognizes abbreviations, takes term frequency into account, and other natural language processes. It will filter through all the content, extracts relevant facts, spot patterns, and provides the researcher with a more condensed set of results and statements than a literature search or a cursory review of abstracts ever could. It knows bladder cancer is a disease state. It knows, in this instance, to look for men as opposed to women. It understands what a biomarker is and how to apply this term to other disease states. It understands bladder cancer is a phrase and not being used as two separate terms. Text mining software involves high level programming and such concepts as word frequency distribution, pattern recognition, information extraction, and natural language processing as well as other programming concepts well beyond the scope of this video. The overall goal is to turn text into data for analysis and thereby help to draw conclusions. However, the results of text mining in and of themselves is not the end product, just part of the process. Individual text mining tools or enterprise level ones have become more common with researchers, librarians, and large for profit and not for profit organizations, and they will only grow. Aside from a text mining tool, an application is also necessary to check that the content being mined is licensed and to provide appropriate links to the content. Text mining is important to publishers or any group that holds large stores of full text articles or databases because this information as a whole has greater value than each individual part. Text mining can help extract that value. A key point for publishers is that the text mining tool and its user, such as a researcher, needs to have access to the content either by it being open access, through a subscription, or through a purchase. Subscription publishers see revenue when content is accessed or purchased. All publishers see article downloads and page views from text mining efforts. Either way, text mining as a tool in research, in medicine, in pharmaceutical R&D will only continue to grow in importance. Well that’s it. Please subscribe to my YouTube channel or click on the playlist to see more videos about text mining in scholarly publishing. And make comments below or email me with questions. Thank so much and take care.
Views: 321 John Bond
Twitter Sentiment Analysis - Natural Language Processing With Python and NLTK p.20
 
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Finally, the moment we've all been waiting for and building up to. A live test! We've decided to employ this classifier to the live Twitter stream, using Twitter's API. We've already covered how to do live Twitter API streaming, if you missed it, you can catch up here: http://pythonprogramming.net/twitter-api-streaming-tweets-python-tutorial/ After this, we output the findings to a text file, which we intend to graph! 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: 87113 sentdex
Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 19133 Microsoft Research
BADM 1.1: Data Mining Applications
 
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This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 3547 Galit Shmueli
Prof. Ronen Feldman - Sentiment Analysis with applications to Finance and Marketing
 
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Ministry of Science, Technology and Space, Hebrew University's Center of Knowledge for Machine Learning and Artificial Intelligence
Detecting Emotion, Intent, Deception and other Signals in Text
 
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Webinar Abstract: All sentiment analysis systems can deliver positive/negative/neutral classifications. But there are many other useful signals in text: emotion, intent, speculation, risk, etc. This talk will present a survey of relevant techniques and the state of the art in recognising these other dimensions of sentiment in text and describe some practical and some potential applications in finance and elsewhere. Presenter: Stephen Pulman; TheySay Analytics Presenter CV: Stephen Pulman is Professor of Computational Linguistics at the Department of Computer Science, Oxford University. He is a Professorial Fellow of Somerville College, Oxford, and a Fellow of the British Academy. He has also held visiting professorships at the Institut für Maschinelle Sprachverarbeitung, University of Stuttgart; and at Copenhagen Business School. He is a co-founder of TheySay Ltd. Previous positions include Professor of General Linguistics at Oxford University, Assistant Professor (Reader) at the University of Cambridge Computer Laboratory, and Director of SRI International's Cambridge Computer Science Research Center.
Views: 1377 UNICOM Seminars
Data Mining Applications
 
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watch and share the video....
Views: 60 Binju Thomas
Data Mining in Finance - How is Data Mining Affecting Society?
 
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Title of Project/Presentation: Data Mining in Finance - How is Data Mining Affecting Society? Individual Subtopic: Finance Abstract of Presentation/Paper: In today’s society a vast amount of information is being collected daily. The collection of data has been deemed useful and is utilized by many sectors to include finance, health, government, and social media. The finance sector is vast and is implemented in things such as: financial distress prediction, bankruptcy prediction, and fraud detection. This paper will discuss data mining in finance and its association with globalization and ethical ideologies. Description of tools and techniques used to create the presentation: Power Point http://screencast-o-matic.com/
Views: 1446 Gregory Rice
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
Deep Learning for Financial Sentiment Analysis
 
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Author: Sahar Sohangir, Florida Atlantic University More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 1107 KDD2016 video
Analytics - Job Types and Applications used
 
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In this session we discuss about the Analytics as a Recent Career Opportunity and the Types of Jobs and the Applications used for the purpose in Analytics. About the Trainer Subhashini has over 12 years of experience in Analytics, Evidence Based Management and Training, and has worked with reputed companies like Citi GDM, GE Money, Standard Chartered Bank and Tata Motors Finance Ltd. About Us: Academy of Financial Training is training services company that specializes in providing a complete range of finance training services and solutions Since its incorporation AFT has trained more than 5,000 attendees in various finance domains, and is serving marquee Fortune 500 clients, making it one of the largest corporate training companies in India AFT's training modules include programs right from basic financial statements analysis to advanced financial modelling, corporate finance, risk management and capital markets, etc related trainings. SUBSCRIBE for Updates on our Upcoming Training Videos Visit us: http://www.ftacademy.in/
Data mining applications and techniques
 
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1.Business Sector 2.Marketing and Retailing sector 3.Bio informatics 4.Climatology 5.Banking and Finance 6.Security and Data Integrity 7.E-commerce 8.Forensic and Criminal Investigation 9.Goverment Records 10.Cloud computing
Views: 825 Karthiga Ganesan
VoC Analysis through Text Mining
 
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Join executives from Taco Bell and Megaputer Intelligence as they present a compelling case study focused on analysis of Voice of Customer data that allowed Taco Bell to improve the operation of their restaurants nationwide. For over 3 years, Taco Bell Corporation collected Voice of Customer data through multiple channels and analyzed it using Megaputer’s PolyAnalyst™, a leading data and text analytics software. Over two million individual customer comments were analyzed to provide rich insights on product, service and facility topics. The impact of overall satisfaction was measured for each topic area in order to provide focus for the operations of the restaurant. Speakers: Sergei Ananyan (CEO) Megaputer Intelligence Jon Frey (Retired Dir of Operations Intelligence) Taco Bell
15 Hot Trending PHD Research Topics in Data Mining 2018
 
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15 Hot Trending Data Mining Research Topics 2018 1. Medical Data Mining 2. Education Data Mining 3. Data Mining with Cloud Computing 4. Efficiency of Data Mining Algorithms 5. Signal Processing 6. Social Media Analytics 7. Data Mining in Medical Science 8. Government Domain 9. Financial Data Analysis 10. Financial Accounting Fraud Detection 11. Customer Analysis 12. Financial Growth Analysis using Data Mining 13. Data Mining and IOT 14. Data Mining for Counter-Terrorism Key Research Application Fields: • Crisp-DM • Oracle Data mining • Web Mining • Open NN • Data Warehousing • Text Mining WHY YOU NEED TO OUTSOURCE TO PhD Assistance: a) Unlimited revisions b) 24/7 Admin Support c) Plagiarism Generate d) Best Possible Turnaround time e) Access to High qualified technical coordinators and expertise f) Support: Skype, Live Chat, Phone, Email Contact us: India: +91 8754446690 UK: +44-1143520021 Email: [email protected] Visit Webpage: https://goo.gl/HwJgqQ Visit Website: http://www.phdassistance.com
Views: 6005 PhD Assistance
Bank Marketing Data Mining Project using KNIME
 
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This is the presentation for the Data Mining Project done using Bank Marketing data set for subject 31005 Advance Data Analytic.
Views: 2728 Sunish Manandhar
Making Predictions with Data and Python : Predicting Credit Card Default | 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/2eZbdPP]. Demonstrate how to build, evaluate and compare different classification models for predicting credit card default and use the best model to make predictions. • Introduce, load and prepare data for modeling • Show how to build different classification models • Show how to evaluate models and use the best to make predictions 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: 35674 Packt Video
Financial Data Mining - Group 2
 
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Fall 2016, UConn
Views: 105 Sudeep Bapat
Application of Data Mining in Business Management | Basic Concepts of Data Mining
 
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There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyze this huge amount of data and extract useful information from it. Extraction of information is not the only process we need to perform; data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation. Once all these processes are over, we would be able to use this information in many applications such as Fraud Detection, Market Analysis, Production Control, Science Exploration, etc. What is Data Mining? Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. The information or knowledge extracted so can be used for any of the following applications − Market Analysis Fraud Detection Customer Retention Production Control Science Exploration Data Mining Applications Data mining is highly useful in the following domains − Market Analysis and Management Corporate Analysis & Risk Management Fraud Detection Apart from these, data mining can also be used in the areas of production control, customer retention, science exploration, sports, astrology, and Internet Web Surf-Aid 🧐 What we are going to Cover in the Video: 🧐 0:00 - 4: 35 Introduction to Data Mining 4:36 - 7:09 What is Data / Data vs. Information 7:09 - 9:13 What is Data Mining 10:00 -11: 00 Data Mining Process 9:14 - 11:45 Why Data Mining 12:04 - 14: 00 Application of data mining
Views: 652 UpDegree
Text Mining Social Media Sentiment Analytics in  R-11th June 2016
 
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Analytics Accelerator Program- May 2016-July 2016 Batch
Text Mining & Libraries: What can we learn from HathiTrust, digital scholars, and the ASHE project?
 
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A discussion of libraries' use of full-text metadata for research including copyright issues, scholars' text mining practices, and lessons to be learned from ASHE (Automatic Subject Heading Extraction), a project that has already used text mining to enhance discovery.
Views: 1187 Harvard University
Unstructured Data for Finance
 
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Financial analysis techniques for studying numeric, well structured data are very mature. While using unstructured data in finance is not necessarily a new idea, the area is still very greenfield. On this episode,Delia Rusu shares her thoughts on the potential of unstructured data and discusses her work analyzing Wikipedia to help inform financial decisions. Delia's talk at PyData Berlin can be watched on Youtube (Estimating stock price correlations using Wikipedia). The slides can be found here and all related code is available on github.
Views: 179 Data Skeptic
text mining, web mining and sentiment analysis
 
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text mining, web mining
Views: 1646 Kakoli Bandyopadhyay
Machine Learning, News Analytics, and Stock Selection
 
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Slides available ► https://goo.gl/Sb5RJu Full Event ► https://goo.gl/LvnmwY Yin Luo, Managing Director, Global Head of Quantitative Strategy, Deutsche Bank. Big data and machine learning have generated tremendous interest in empirical finance research. In this paper, we study a unique news analytics database provided by Ravenpack. We apply a suite of innovative machine learning algorithms, including adaBoost, spline regression, and other boosting/bagging techniques on both traditional and unstructured news data in predicting stock returns. We find news sentiment data adds significant incremental predictive power to our machine learning based global stock selection models. Session recorded June 16, 2016 at the RavenPack 4th Annual Research Conference, titled "Reshaping Finance with Alternative Data". Watch all sessions: ► https://goo.gl/3ij1Ev Visit us at ►https://www.ravenpack.com/ Follow RavenPack on Twitter ► https://twitter.com/RavenPack #RavenPack #finance #sentiment #newsanalytics #bigdata
Views: 7564 RavenPack
Stock Price Prediction | AI in Finance
 
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Can AI be used in the financial sector? Of course! In fact, finance was one of the pioneering industries that started using AI in the early 80s for market prediction. Since then, major financial firms and hedge funds have adopted AI technologies for everything from portfolio optimization, to credit lending, to stock betting. In this video, we'll go over all the different ways AI can be used in applied finance, then build a stock price prediction algorithm in python using Keras and Tensorflow. Code for this video: https://github.com/llSourcell/AI_in_Finance Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1 https://www.datacamp.com/community/tutorials/finance-python-trading http://www.cuelogic.com/blog/python-in-finance-analytics-artificial-intelligence/ https://www.udacity.com/course/machine-learning-for-trading--ud501 https://www.oreilly.com/learning/algorithmic-trading-in-less-than-100-lines-of-python-code Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 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: 192291 Siraj Raval
Let's Get Rich With quantmod And R! Rich With Market Knowledge! Machine Learning with R
 
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See how easy it is to download, visualize and manipulate daily stock market data and how to use it to build a complex market model. Code and walkthrough: http://amunategui.github.io/wallstreet/ Note: for those that can't use XGBoost - I added an alternative script using GBM in the walkthrough: http://amunategui.github.io/wallstreet/ Top of the page under resources look for link: "Alternative GBM Source Code - for those that can't use xgboost" MORE: Signup for my newsletter and more: http://www.viralml.com Connect on Twitter: https://twitter.com/amunategui My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: https://amzn.to/2PV3GCV Grow Your Web Brand, Visibility & Traffic Organically: 5 Years of amunategui.github.Io and the Lessons I Learned from Growing My Online Community from the Ground Up: Fringe Tactics - Finding Motivation in Unusual Places: Alternative Ways of Coaxing Motivation Using Raw Inspiration, Fear, and In-Your-Face Logic https://amzn.to/2DYWQas Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK Defense Against The Dark Digital Attacks: How to Protect Your Identity and Workflow in 2019: https://amzn.to/2Jw1AYS CATEGORY:DataScience HASCODE:True
Views: 45118 Manuel Amunategui
R programming language with applications in Finance and Econometrics - 01 - 02
 
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An Introduction to the R programming language with applications in Finance and Econometrics 1ª parte: Basics on R Profª. Beatriz Pateiro (Univ. Santiago de Compostela) Período: 13 a 17 de maio de 2013 AULA 02 Dentro do contexto de software livre e colaborativo, a linguagem de programação R se tornou um padrão fundamental em análise e tratamento de dados, bem como em aplicações ao cálculo numérico, principalmente em aplicações às finanças e a dados econométricos. Ela rapidamente substituiu a linguagem proprietária S e muitos programas que funcionam para o S, podem ser rapidamente adaptados para R. Além disso, possui muitas interfaces amigáveis. A importância desta linguagem para aplicações estatísticas e tratamento de dados é tal que diversos grupos vem concentrando substancial esforço para o desenvolvimento de algoritmos e softwares adequados. Página do evento: http://www.impa.br/opencms/pt/ensino/calendarios/2013/Minicursos/an_introduction_R_programming_language_with_applications_finance_econometrics.html Download dos Vídeos: http://video.impa.br/index.php?page=2013---an-introduction-to-the-r-programming-language-with-applications-in-finance-and-econometrics IMPA - Instituto Nacional de Matemática Pura e Aplicada © http://www.impa.br | http://video.impa.br.
Python for Economists: An overview of Python tools for Economists
 
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Gary Hlusko http://www.pyvideo.org/video/3702/python-for-economists-an-overview-of-python-tool http://pyohio.org/schedule/presentation/167/ Python has developed applications in GIS, text analysis, networks, statistics, csv manipulation, data analysis, data mining and simulations. Despite this, there are few references for using python as an economist. This talk provides an introduction to economic tools using python. I conclude with python in data analysis and future projects for economists using python.
Views: 4985 Next Day Video
Text Analytics Webinar by Imarticus Learning organised by DXC Technology
 
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Imarticus Learning invited the team from DXC Technology to conduct a webinar on Text Analytics. The webinar focuses on the opportunity of untapped textual data and the need for text mining in the industry of Analytics and Research. Visit to know more: http://imarticus.org/
Views: 333 Imarticus Learning
Towards Concept-Based Text Understanding and Mining
 
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Key words: Statistical Natural Language Processing, Machine Learning, Text Mining, and Semantic Integration Most information in the world exists in the format of text, such as news articles and web pages. Different lines of research have been conducted to allow a computer system to pinpoint knowledge from text with different precision, including information retrieval, information, and question answering. The goal of these tasks is to discover, understand and access knowledge about real-world entities and relations from text. Currently, however, most of them are still relying on simple string and token-level matching techniques to identify relevant information, and extracted information are typically not effectively integrated. In this talk, we explain the necessity of moving from string and token processing to concept-based text understanding and mining, and that of integrating information based on real-world concepts. Furthermore we provide state-of-the-art solutions to the related problems. At the heart of this work is a mechanism, I-Track, that can automatically resolve the concept ambiguity in text: a given entity -- representing a person, a location or an organization -- may be mentioned in text in multiple, ambiguous ways. This talk will cover three related projects that we have been working on in the last few years: Entity identification in text, Semantic integration across text and databases, Supervised Clustering Framework for entity identification.
Views: 104 Microsoft Research
DBSCAN Clustering Easily Explained with Implementation
 
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Density-based spatial clustering of applications with noise (DBSCAN) is a well-known data clustering algorithm that is commonly used in data mining and machine learning. Based on a set of points (let’s think in a bidimensional space as exemplified in the figure), DBSCAN groups together points that are close to each other based on a distance measurement (usually Euclidean distance) and a minimum number of points. It also marks as outliers the points that are in low-density regions. #DBSCANclustering Github Link: https://github.com/krishnaik06/DBSCAN-Algorithm You can buy my book on Finance with ML and DL from amazon Amazon url :https://www.amazon.in/Hands-Python-Finance-implementing-strategies/dp/1789346371/ref=sr_1_1?keywords=Krish+naik&qid=1559746413&s=books&sr=1-1
Views: 423 Krish Naik
Data Science in 8 Minutes | Data Science for Beginners | What is Data Science? | Edureka
 
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** Data Scientist Masters Program: https://www.edureka.co/masters-program/data-scientist-certification ** This edureka video on Data Science will introduce you to the concepts of Data Science and how it is used to solve real-world problems. You will learn how Data Science works with an example on UBER. Data is everywhere, and its growing at an exponential rate. So, Data science is the process of using the data to find solutions / to predict outcomes of a problem statement. Below are the topics covered in this video: 0:57 What is Data Science? 1:11 How Data Science works? : Data Science at UBER 2:06 Data Science Process a. Business Requirements b. Data Collection c. Data Cleaning d. Data Exploration and Analysis e. Data Modelling f. Data Validation g. Deployment and Optimization 4:17 Data Science Applications 6:05 Who is a Data Scientist? 6:18 Data Scientist Job Trends 6:51 Data Scientist Skills Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS Machine Learning Podcast: https://castbox.fm/channel/id1832236 Instagram: https://www.instagram.com/edureka_learning/ Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #DataScienceEdureka #whatisdatascience #Datasciencein8minutes #Datasciencecourse #datascience - - - - - - - - - - - - - - About the Master's Program This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles. - - - - - - - - - - - - - - Topics Covered in the curriculum: Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc. - - - - - - - - - - - - - - For more information, please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free)
Views: 29110 edureka!
The Pillars of Natural Language Processing for Finance
 
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Peter Hafez, Chief Data Scientist at RavenPack, discusses the pillars of Natural Language Processing within the financial sector. RavenPack is the leading big data analytics provider for financial services. Financial professionals rely on RavenPack for its speed and accuracy in analyzing large amounts of unstructured textual content. The company’s products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating data-driven insights on news, social media and proprietary textual content in their models or workflows. RavenPack’s clients include the most successful hedge funds, banks, and asset managers in the world. https://www.ravenpack.com/
Views: 237 RavenPack
Hidden Markov Models
 
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This video is part of the Udacity course "Introduction to Computer Vision". Watch the full course at https://www.udacity.com/course/ud810
Views: 103647 Udacity
AI in Finance use cases
 
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Seldon Machine Learning Strategy Breakfast 28th Feb 2018
Views: 355 Seldon
The Best PGP in Data Science, Business Analytics & Big Data in association with IBM
 
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Launch your career in the fastest growing and highest paid areas of Data Science, Big Data, Business Analytics, Predictive Analytics, Machine Learning, Deep Learning, NLP, Cognitive Computing, R, Python, Hadoop, Spark, IBM Cognos, IBM Watson, Tableau, Visualization, Marketing Analytics, Financial Analytics, Operations Analytics etc Join India's Best PGP in Data Science, Business Analytics & Big Data in association with IBM. Full Time program https://www.muniversity.mobi/PGP-DataScience/ Executive Part Time Program https://www.muniversity.mobi/Weekend-EPGP-DataScience/ Executive Online Program https://www.muniversity.mobi/Online-EPGP-DataScience/ Highlights of the Program : Hundreds of dreamers have become Data Scientist, Business Analyst, Data Analyst, Machine Learning engineers, Risk Analyst, Deep Learning Engineers etc. ✓ Final PGP Certification by IBM ✓ Additional Certification in Deep Learning by NVIDIA DLI ✓ Program delivered with association of IBM, AWS Educate, NVIDA DLI and UBTech a leading company for Robotics. ✓ Program delivered by IBM experts and Best Data Scientist ✓ Live Industry Projects ✓ Globally accepted credit structure ✓ Career Management Center (CMC), Job Placement and Internship ✓ Min 6.5 LPA, Freshers Minimum; 8.5 LPA; Freshers Average; 13 LPA, Freshers Highest; 40 LPA, Experience Highest; Upto 300% Experience Candidates Hike ✓ Best placement: over 45 companies in last placement season ✓ Best Data Science curriculum ✓ Scholarships and Financial Aid for meritorious students ✓ World-class LMS on Cloud ✓ CMC, Internship & Placement: Career Management Center (CMC) at Aegis facilitates all students placement and paid internship opportunities. Paid internship are for 2 to 3 months with various companies to give them reallife live experience which generally leads to final placement as role of Data Scientist, Manager Data Science, Business Analyst, Risk Analyst etc like Accenture, Atos, Deloitte, E&Y, PwC, Fractal, Angel Broking, Cybage, edelweiss, Teradata, HDFC, Ford Automotive, Mercedes-Benz, Bank of America, VM Ware, IBM, Aditya Birla, Suzlon, Eclerx, Aureus Analytics, Clover Infotech, Value Direct, Virtusa, Credit Vidya, Shzertech, Loginext, Persistent, L & T Finance, Mobiliya, Emmfer, Infrasoft, Impact Analytics, Eigen Technologies, Intelenet, Pentation, Ixsight, Softcell, Easy Farm, Exponentia Data Labs, Open Insights, Kryptoblocks, Bayer, Cuddle AI, Wipro, TechMahnidra, Cognizent, TCS, Reliance Jio etc.
Views: 13421 Aegis TV
Philip Brittan - Wrangling and Evaluating Financial Datasets
 
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PyData New York City 2017 In Information Supply Chain Logistics there is a demand to help companies discover relevant sources of data and help them evaluate that data for fitness to the needs of their use cases. We share our insights in orchestration of the supply chain of financial datasets to wrangle and ultimately evaluate the data itself.
Views: 738 PyData
Text By the Bay 2015: Kang Sun, Teaching Machines to Read for Fun and Profit
 
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Kang Sun from the R&D Machine Learning group will speak about Bloomberg’s current projects in the area of Machine Learning and Natural Language Processing, such as sentiment analysis of financial news, market impact predictions, question answering, etc. There will be a discussion of future directions and as well as a Q&A session. In this talk Kang Sun from the R&D Machine Learning group at Bloomberg will speak about current projects involving Machine Learning and applications such as Natural Language Processing. We will discuss the evolution and development of several key Bloomberg projects such as sentiment analysis, market impact prediction, novelty detection, social media monitoring, question answering and topic clustering. We will show that these interdisciplinary problems lie at the intersection of linguistics, finance, computer science and mathematics, requiring methods from signal processing, machine vision and other fields. Throughout, we will talk about practicalities of delivering machine learning solutions to problems of finance and highlight issues such as importance of appropriate problem decomposition, feature engineering and interpretability. There will be a discussion of future directions and applications of Machine Learning in finance as well as a Q&A session. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 368 FunctionalTV
The 7 Reasons Most Machine Learning Funds Fail Marcos Lopez de Prado from QuantCon 2018
 
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This talk, titled The 7 Reasons Most Machine Learning Funds Fail, looks at the particularly high rate of failure in financial machine learning. The few managers who succeed amass a large number of assets, deliver consistently exceptional performance to their investors. However, that is a rare outcome. This presentation will go over the 7 critical mistakes underlying most financial machine learning failures based off of Marcos López de Prado’s experiences and observations. To learn more about Quantopian, visit http://bit.ly/mlqc2018. The slides for this presentation can be found at http://bit.ly/2DyUNdc. Bio of the Speaker: Dr. Marcos López de Prado is the chief executive officer at True Positive Technologies LP. He founded Guggenheim Partners’ Quantitative Investment Strategies (QIS) business, where he applied cutting-edge machine learning to the development of high-capacity strategies that delivered superior risk-adjusted returns. After managing up to $13 billion in assets, López de Prado acquired QIS and successfully spun out that business in 2018. López de Prado is a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). A top 10-most-read author in finance based on SSRN's rankings, he has published dozens of scientific articles on machine learning and supercomputing and holds multiple international patent applications on algorithmic trading. Marcos earned a Ph.D. in Financial Economics (2003), a Ph.D. in Mathematical Finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University. Disclaimer Quantopian provides this presentation to help people write trading algorithms - it is not intended to provide investment advice. More specifically, the material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Quantopian. In addition, the content neither constitutes investment advice nor offers any opinion with respect to the suitability of any security or any specific investment. Quantopian makes no guarantees as to accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.
Views: 8462 Quantopian
Quantitative Trading using Sentiment Analysis by Rajib Ranjan Borah - June 28, 2016
 
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Date and Time: Tuesday, June 28, 2016 04:00 PM GMT | 12:00 PM EST | 09:30 PM IST About Sentiment Analysis: Sentiment Analysis. also known as opinion mining, is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral. The analysis finds significant prominence in social media, stock markets, law, policy making, sociology and even customer service. Session Contents: - How Sentiment Analysis works - Designing Trading Strategies using Sentiment Analysis - Historical analysis of profitability – Case studies - Pitfalls in using Sentiment Analysis and how to avoid them Speaker: Rajib Ranjan Borah - Co-founder, iRageCapital Advisory Rajib designs High Frequency Trading Strategies for Asian exchanges and works with other exchanges & institutions to design educational programs. He has been an invited speaker at conferences like “UChicago Quant Trading Conference” and “5th Annual Conference: Behavioural Models & Sentiment Analysis Applied to Finance”, July 2015 in London, on sentiment analysis and high frequency trading in America, Europe and Asia. Most Useful links Join EPAT – Executive Programme in Algorithmic Trading : https://goo.gl/3Oyf2B Visit us at: https://www.quantinsti.com/ Like us on Facebook: https://www.facebook.com/quantinsti/ Follow us on Twitter: https://twitter.com/QuantInsti
Developing Trading Models Using Machine Learning on Financial News and Social Media Data
 
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Webinar presented by Richard Peterson, CEO of MarketPsych Data.
Views: 337 UNICOM Seminars