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Text mining with Voyant Tools, no R or any other coding required
 
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Please explore free and beautiful Voyant Tools that allow you to perform any text analysis or even mining - word frequency, clouds, co-occurrence (collocations), spider diagrams, context analysis - anything you dreamt of without any prior programming experience or need to buy expensive software. To those interested in reproducing what we've done and further analyzing comments to Indian political articles (dated March-April and January 2016), please use this link to get the ball rolling: http://voyant-tools.org/?corpus=0c17d82dbd8b04baae655f90db84a672 Lastly, creators of the video are eternally grateful to our Big Data class professor, who believed in us and kept us going despite any technical or analytical difficulties.
Views: 8264 Adventuruous Mind
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: 52333 edureka!
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: 45691 DeepLearning.TV
Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka
 
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** Natural Language Processing Using Python: https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry. NLP Tutorial : https://www.youtube.com/watch?v=05ONoGfmKvA Subscribe to our channel to get video updates. Hit the subscribe button above. ------------------------------------------------------------------------------------------------------- #NLPin10minutes #NLPtutorial #NLPtraining #Edureka 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 learned 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: 58363 edureka!
Introduction to Text Analytics with R: Overview
 
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The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and initial data cleaning – Some initial data analysis, feature engineering, and data visualization About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz5_y0 Watch the latest video tutorials here: https://hubs.ly/H0hz61V0 See what our past attendees are saying here: https://hubs.ly/H0hz6-S0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 74340 Data Science Dojo
How To Analyze Your Customer Reviews with Text Analysis - Data Crunch
 
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Add in-demand data science skills to your resume: https://www.thelead.io/data-science-360/ If you run a business or organizations, it's important to know what your customers are saying about you - whether if its a form of a blog, social media post, review or comment. This is where text mining and text analysis comes into play. Data scientists build text mining algorithms to mine texts from customers and map out a word cloud to understand their customers. Dr. Lau shows us how to do text analysis in this Data Crunch episode. Text analysis data files for this episode: https://goo.gl/Y5YwRH Google Alerts: https://www.google.com.my/alerts Brandwatch: https://www.brandwatch.com/ =============== Where to follow and learn more from LEAD: Website: https://www.thelead.io Facebook: https://www.facebook.com/thelead.io/ Instagram: https://www.instagram.com/theleadio/ ================ LEAD is an institute in Malaysia, where we provide courses in Data Science, Full Stack Web Development, Digital Marketing & Business, for individuals and corporates — so they can find better careers or to build successful businesses. We teach career-ready skills that our students can use right away in their jobs or find a job. Rather than taking years to learn and master a subject, we have designed our courses to shortcut our students to be competent in the workspace. So we gathered a group of experts in their fields, to teach and mentor our students. Collectively, our 15+ years in technology mentoring means you’ll get real insights & strategies from the best developers, digital marketers, and data scientists.
Views: 526 LEAD
QDA Miner Lite - Free Qualitative Data Analysis Software
 
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In this video, we are going to show you how to use QDA Miner Lite to code and analyze your documents and images. QDA Miner Lite is a free qualitative data analysis software made by Provalis Research.
Text Analytics Software, What Is It and Why is It Worth $1.8 Billion?
 
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Best binary Options broker: http://bit.ly/1zS1i44 looks at text analytics software and why IBM bought Netezza for $1.7 billion recently. Text mining or text digging has been around for years, so why is it so valuable now? Check out this video on data mining to see..
How to build a Text Mining Platform
 
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Tiger Zhang & Lutz Finger on Text Mining Today more than ever before, we have access raw data in the form of texts. Businesses around the world store text discussions from their market research, customer care discussions, or brand relevant conversation on social media. While it is clear that texts contain valuable information, it is often less clear on how best texts can be analyzed at scale. In this class, we will share how we at LinkedIn built a scalable text-mining platform to uncover insights from text data. We will focus on two important components: THEME DISCOVERY of new content and how to CLASSIFY existing text. Using both features, we can detect emerging trends within reviews, customer care discussions and market research data. You will learn: THEME DISCOVERY - information extraction Theme recognition is a highly complex task due to the multi-facetted nature of our language. Theme Recognition (without requiring manual reviews) is, however, the main component of any text-mining platform. We will introduce an innovation in information extraction using part of speech tagging (currently patent pending) to uncover themes within textual data. TEXT CLASSIFICATIONS - Supervised Machine Learning Another important component of our NLP platform is the ability to classify text via supervised machine learning algorithms such as support vector machine (SVM). The ability to classify serves many business use-cases ranging from sentiment analytics to product identification. You will learn in our talk how to cater to those different requirements via a flexible platform setup. VALUE of DATA - Member Feedback The combined ability of Themes Discovery (new content and ideas) as well as Classifications (standard measure) creates a very effective framework to get business insights out of text data. We will demonstrate this on the use case of classifying and responding to member feedback.
Views: 12788 Lutz Finger
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 281499 Siraj Raval
Introduction to text mining with Voyant
 
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In this introduction to text mining with Voyant I cover: 1) Data cleaning (text editors, Notepad++ and Sublime Text) 2) Loading your text into Voyant 3) Expectations, what Voyant can and cannot do 4) Working with common visualization tools and making possible connections 5) Exporting visualizations
Introduction to Text Analysis with NVivo 11 for Windows
 
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It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share. http://www.qsrinternational.com
Views: 147736 NVivo by QSR
How to easily perform text data content analysis with Excel
 
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Perform complex text analysis with ease. Automatically find unique phrase patterns within text, identify phrase and word frequency, custom latent variable frequency and definition, unique and common words within text phrases, and more. This is data mining made easy. Video Topics: 1) How to insert text content data for analysis 2) Perform qualitative content analysis on sample survey 3) Review text content phrase themes and findings within data 4) Review frequency of words and phrase patterns found within data 5) Label word and phrase patterns found within data
Views: 62216 etableutilities
Why You Should Do Text Analysis in Python (Even if You Don't Want to) - Bhargav Srinivasa Desikan
 
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PyData LA 2018 The explosion in Artificial Intelligence and Machine Learning is unprecedented now - and text analysis is likely the most easily accessible and understandable part of this. And with python, it is crazy easy to do this - python has been used as a parsing language forever, and with the rich set of NLP, ML and Computational Linguistic tools, it's worth doing text analysis even if you don't want to. --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1149 PyData
Tips, Tricks and Topics in Text Analysis - Bhargav Srinivasa Desikan
 
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PyData LA 2018 Not only is there an abundance of textual data, there is also an abundance of tools help analyse this data - and it is tough to choose the right tool for the right task. In this workshop we will be dealing with the entire text analysis process - this means we'll start with finding data, set up a pipeline to clean our text, annotate it, and then have it ready to do some more advanced analysis. Repo - https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 914 PyData
Performing Sentiment Analysis
 
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Ever wonder how to analyze people’s words to gauge sentiment? In this video, we’ll show you how! You’ll learn: - How to gather sentiment data - How to clean and structure it - How to perform the analysis in Tableau
Views: 9760 Tableau Software
Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 13755 Stat Pharm
Text Mining In R | Natural Language Processing | Data Science Certification Training | Edureka
 
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** Data Science Certification using R: https://www.edureka.co/data-science ** In this video on Text Mining In R, we’ll be focusing on the various methodologies used in text mining in order to retrieve useful information from data. The following topics are covered in this session: (01:18) Need for Text Mining (03:56) What Is Text Mining? (05:42) What is NLP? (07:00) Applications of NLP (08:33) Terminologies in NLP (14:09) Demo Blog Series: http://bit.ly/data-science-blogs Data Science Training Playlist: http://bit.ly/data-science-playlist - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka - - - - - - - - - - - - - - - - - #textmining #textminingwithr #naturallanguageprocessing #datascience #datasciencetutorial #datasciencewithr #datasciencecourse #datascienceforbeginners #datasciencetraining #datasciencetutorial - - - - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyze Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyze data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies. For online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
Views: 5379 edureka!
Sentiment Analysis in Excel
 
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Recorded with http://screencast-o-matic.com
Views: 10102 marketingprofessor
SAS® Text Analytics Software Demo
 
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http://www.sas.com/en_us/software/analytics/text-miner.html SAS Text Analytics help companies address big data issues that arise from unstructured content by applying linguistic rules and statistical methods. SAS TEXT MINER Get faster, deeper insight from unstructured data. Why limit yourself to analyzing legacy data? Our text mining software lets you easily analyze text data from the web, comment fields, books and other text sources. Discover new information, topics and term relationships that deepen your understanding. And add what you learn to your models to improve lift and performance. Benefits: * Improve model performance. * Add subject-matter expertise. * Automatically know more. * Determine what's hot and what's not. LEARN MORE ABOUT SAS TEXT MINER http://www.sas.com/en_us/software/analytics/text-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss To learn more about SAS Text Analytics, visit http://www.sas.com/textanalytics
Views: 25105 SAS Software
Tricks, tips and topics in Text Analysis - Bhargav Srinivasa Desikan
 
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PyData Amsterdam 2018 There is an abundance of easily mineable text data (Whatsapp, twitter, and even our own e-mails!), and we have no excuse to not analyse it. In this workshop, we will learn some tips and tricks to deal with messy text data, before moving on to some lesser looked at text analysis techniques, such as text summarisation, working with distance metrics, and an old personal favorite - topic models. Slides: https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial -- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1438 PyData
Text Mining with XLMiner in Excel
 
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This is Part 5 of 7. Use XLMiner in Excel for all your text mining needs. Go here to see the full playlist http://youtu.be/ehe2RYnBcXs?list=PLTMrxZeq4dutOgKhXw8ZcYf7oRzbKYe1p
Views: 4838 FrontlineSolvers
Tricks, tips and topics in Text Analysis - Bhargav Srinivasa Desikan
 
01:28:19
PyData Berlin 2018 There is an abundance of easily mineable text data (Whatsapp, twitter, and even our own e-mails!), and we have no excuse to not analyse it. In this workshop, we will learn some tips and tricks to deal with messy text data, before moving on to some lesser looked at text analysis techniques, such as text summarisation, working with distance metrics, and an old personal favorite - topic models. --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1091 PyData
Text Mining for Beginners
 
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This is a brief introduction to text mining for beginners. Find out how text mining works and the difference between text mining and key word search, from the leader in natural language based text mining solutions. Learn more about NLP text mining in 90 seconds: https://www.youtube.com/watch?v=GdZWqYGrXww Learn more about NLP text mining for clinical risk monitoring https://www.youtube.com/watch?v=SCDaE4VRzIM
Views: 78708 Linguamatics
Sentiment Analysis in R | Sentiment Analysis of Twitter Data | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Sentiment Analysis Tutorial shall give you a clear understanding as to how a Sentiment Analysis machine learning algorithm works in R. Towards the end, we will be streaming data from Twitter and will do a comparison between two football teams - Barcelona and Real Madrid (El Clasico Sentiment Analysis) Below are the topics covered in this tutorial: 1) What is Machine Learning? 2) Why Sentiment Analysis? 3) What is Sentiment Analysis? 4) How Sentiment Analysis works? 5) Sentiment Analysis - El Clasico Demo 6) Sentiment Analysis - Use Cases Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #SentimentAnalysis #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 33313 edureka!
Text mining using Excel, Semantria, and Tableau
 
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In less than 5 minutes, we take 20,000 tweets from Datasift, perform text mining through the Lexalytics/Semantria Excel Plugin, import the results into Tableau, and start visualizing cool stuff. (This process applies to Tableau version 8.2).
Views: 19742 Lexalytics
Text Analysis in Power BI with Cognitive services with Leila Etaati
 
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Abstract: Data that we collected always is not about numbers and structured data. In any organization, there is a need to analyze the text data such as customer comments, extract the primary purpose of a call from its scripts, detect the language of customer feedback and translate it and so forth. To address this issue, Microsoft Cognitive Services provides a set of APIs, SDKs, and services available to developers to do text analysis without writing R or Python codes. In this session, I will explain what is text analysis such as sentiment analysis, key phrase extraction, Language detection and so forth. Next, the process of text analysis in Power BI using cognitive services will be demonstrated. Follow us on Twitter - https://twitter.com/mspowerbi More questions? Try asking the Power BI Community @ https://community.powerbi.com/
Views: 9761 Microsoft Power BI
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 474942 sentdex
Getting Started with Orange 16: Text Preprocessing
 
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How to work with text in Orange, perform text preprocessing and create your own custom stopword list. For more information on text preprocessing, read the blog: [Text Preprocessing] https://blog.biolab.si/2017/06/19/text-preprocessing/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 22157 Orange Data Mining
SAS Visual Text Analytics Demo
 
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Mary Beth Ainsworth, SAS Global Product Marketing Manager for Text Analytics, and Simran Bagga, Principal Product Manager for Text Analytics at SAS, provide a look at SAS Visual Text Analytics in action. LEARN MORE ABOUT SAS VISUAL TEXT ANALYTICS Get maximum value from your unstructured data using a wide variety of modeling approaches – including supervised and unsupervised machine learning, linguistic rules, categorization, entity extraction, sentiment analysis and topic detection. SAS Visual Text Analytics helps you overcome the challenges of identifying and categorizing large volumes of text data. https://www.sas.com/vta SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW®. VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 5871 SAS Software
Weka text mining
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/sci-indexed-computer-science-journals/
Views: 514 PHD PROJECTS
Rapidminer text mining
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-wireless-sensor-networks/
Views: 70 PHD Projects
Text Mining The Holy Bible With R
 
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Text mining The Holy Bible for its most common words of positive and negative sentiments. I'm fiddlin' around with R and its libraries. Thanks to this book: https://www.tidytextmining.com/ for making text mining easy to understand!
Views: 79 Garrett Gant
How NLP text mining works: find knowledge hidden in unstructured data
 
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Connect with us: http://www.linguamatics.com/contact What use is big data if you can't find what you're looking for? Follow: @Linguamatics https://twitter.com/Linguamatics https://www.linkedin.com/company/linguamatics https://www.facebook.com/Linguamatics https://plus.google.com/+Linguamatics https://www.youtube.com/user/Linguamatics/videos In knowledge driven industries such as the life sciences and healthcare, finding the right information quickly from huge volumes of text is crucial in supporting the best business decisions. However, around 80% of available information exists as unstructured text, and conventional keyword searches only retrieve documents, which still have to be read. This is very time consuming, unreliable, and, when important decisions rest on it, costly. Linguamatics’ text mining solution, I2E, uses Natural Language Processing to identify and extract relevant knowledge at least 10 times faster than conventional search, often uncovering insights that would otherwise remain unknown. I2E analyses the meaning of the text using powerful linguistic algorithms, enabling you to ask open questions, find the relevant facts and identify valuable connections. Going beyond simple keywords, I2E can recognise concepts and the different ways the same thing can be expressed, increasing the recall of relevant information. I2E then presents high quality results as structured, actionable knowledge, enabling fast review and analysis, and providing dramatically improved speed to insight. Our market leading software is supported by highly qualified domain experts who work with our customers to ensure successful project outcomes. Text mining for beginners: https://www.youtube.com/watch?v=40QIW9Sr6Io
Views: 17037 Linguamatics
Text Analytics and Text Mining Explained by OdinText
 
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Text Analytics Explained. Anderson Analytictics, developers of Next Generation Text Analytics software platform OdinText explain Text Analytics and the power of text mining, as well as the difference between first generation text analytics software from IBM SPSS, SAS Text, Attensity and Clarabridge compared to the OdinText Next Generation Text Analytics approach to text and data mining. http://www.OdinText.com
Views: 27308 OdinText
How to create a text mining algorithm with Python
 
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In this talk, we'll cover how use Python to create a text mining algorithm that performs TF IDF analysis to find similar text phrases. We'll also discuss text cleanup techniques, such as stop word removal and stemming. About the speaker: Taylor Steinberg is a software engineer at Premier, Inc focused on data science and machine learning.
Views: 4667 Adam Steinberg
Text mining using rapidminer
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-mobile-cloud-computing/
Views: 717 PHD Projects
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 68293 deltaDNA
Data Visualization & Interactive Data Exploration with KNIME
 
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This video shows some options for data visualization and interactive data exploration, both within KNIME Analytics Platform and from a web browser through the KNIME WebPortal. Here we show: Sunburst chart, box plot, line plot, stacked plot, scatter plot, network graph and a few more visualization techniques. We also show how to control the plots and charts through a slider object and how to exploit the plot/chart interactivity. A reduced version of the workflow shown in this video, and with a smaller data set, can be downloaded from the KNIME EXAMPLES server under 02_Javascript/09_DataVisualization_AirlineDataset. Access the workflow "Data Visualization on Airline Dataset" from the KNIME Community Workflow Hub: https://hub.knime.com/knime/workflows/*nA5-bc885-w_keuN Visit https://www.knime.com
Views: 21550 KNIMETV
Understanding Text using Cognitive Services
 
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You will learn how to get started analyzing text! We’ll show you how to sign up for a cognitive service, and the power of Text Analytics, Entity Linking and Bing Entity Search.
Views: 901 Microsoft Developer
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 165828 Siraj Raval
Text mining with rapidminer
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/thesis-manet-routing-protocols/
Views: 319 PHD Projects
text mining python
 
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Use Python customize the function and import collection and counter to counter the words. Specify the number of occurrences of each word frequency in the text files.
Views: 18 biyi chen
Text Mining and Analytics Made Easy with DSTK Text Explorer
 
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DSTK - Data Science Toolkit offers Data Science softwares to help users in data mining and text mining tasks. DSTK follows closely to CRISP DM model. DSTK offers data understanding using statistical and text analysis, data preparation using normalization and text processing, modeling and evaluation for machine learning and statistical learning algorithms. DSTK Text Explorer helps user to do text mining and text analytics task easily. It allows text processing using stopwords, stemming, uppercase, lowercase and etc. It also has features in sentiment analysis, text link analysis, name entity, pos tagging, text classification using stanford nlp classifier. It allows data scraping from images, videos, and webscraping from websites. For more information, visit: http://dstk.tech
Views: 3646 SVBook
Consuming REST APIs and Text Mining with RapidMiner
 
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Businesses today rely heavily on REST APIs to create and enrich their data sets, and to improve text mining model performance. Yet working with REST APIs in a data science workflow can be cumbersome and challenging. Plus creating topics that best describe natural text from chats or elsewhere adds insight, but with more complexity.
Views: 1695 RapidMiner, Inc.
R PROGRAMMING TEXT MINING TUTORIAL
 
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Learn how to perform text analysis with R Programming through this amazing tutorial! Podcast transcript available here - https://www.superdatascience.com/sds-086-computer-vision/ Natural languages (English, Hindi, Mandarin etc.) are different from programming languages. The semantic or the meaning of a statement depends on the context, tone and a lot of other factors. Unlike programming languages, natural languages are ambiguous. Text mining deals with helping computers understand the “meaning” of the text. Some of the common text mining applications include sentiment analysis e.g if a Tweet about a movie says something positive or not, text classification e.g classifying the mails you get as spam or ham etc. In this tutorial, we’ll learn about text mining and use some R libraries to implement some common text mining techniques. We’ll learn how to do sentiment analysis, how to build word clouds, and how to process your text so that you can do meaningful analysis with it.
Views: 4128 SuperDataScience
Text Mining (part 1)  -  Import Text into R (single document)
 
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Text Mining with R. Import a single document into R.
Views: 22282 Jalayer Academy
Delving into the Q&A network   textmining and graph analysis
 
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Zhen Liang, Xinli Wang Stack Exchange is a Q&A platform where software engineers, scientists, students share knowledge and get questions answered. As users, we are interested in:  - What are heated discussed topics  - How to filtering best answers among all the given answers As developers, we are interested in:  -The problems users are facing and how they can take such information to improve their products and documentation.  Our project addresses such problems by  - Extracting topics out of large amount of posts and the topic distribution of each document  - Predicting the best answers by building a classification model  - Visualizing the “network” of questions, to know what’s the trends and relationships among discussed topics Analytics: - Text mining and feature extraction: NLTK and text mining tools in Python, Spark LDA API with Python and Scala  - Sentiment analysis using AlchemyAPI - Classification algorithms: Random Forest  - Building the graph database in Neo4j  - Visualizations from D3.js toolkit for implementing a visualization to represent the output
Views: 269 Season Liang
Rapidminer text mining tutorial
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topics-wireless-communication/
Views: 664 PHD Projects
Sentiment Analysis using Microsoft Power BI
 
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Overview and demo of using Power BI to do sentiment analysis. The demo covers how you can use Power BI to create a function to call external sentiment analysis APIs and then how the function can be invoked for all the rows in the dataset.
Views: 10684 Melvin L