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Document Classification using Latent semantic analysis (LSA) in python | Sudharsan
 
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Document Classification using Latent semantic analysis (LSA) in python. You can also reach out to me on twitter: https://twitter.com/sudharsan1396 Code for this video: https://github.com/sudharsan13296/Document-Classification-using-LSA
Introduction to Text Analytics with R: VSM, LSA, & SVD
 
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Part 7 of this video series includes specific coverage of: – The trade-offs of expanding the text analytics feature space with n-grams. – How bag-of-words representations map to the vector space model (VSM). – Usage of the dot product between document vectors as a proxy for correlation. – Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics. – How LSA is implemented using singular value decomposition (SVD). – Mapping new data into the lower dimensional SVD space. 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 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/H0hD3WT0 Watch the latest video tutorials here: https://hubs.ly/H0hD3X30 See what our past attendees are saying here: https://hubs.ly/H0hD3X90 -- 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 830 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: 12825 Data Science Dojo
Natural Language Processing with Python: Starting with Latent Semantic Analysis | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2Em9f6d]. This section introduces latent semantic analysis and explains how it can be used to classify text datasets. We begin the LSA example by importing the native NLTK Reuters dataset. Then we introduce and implement a technique to create a weighted vectorization of the text dataset in preparation for more advanced analysis like clustering and classification. • Launch Jupyter Notebook and import NLTK library • Import Reuter’s dataset to demonstrate the analysis • Implement term frequency and inverse term frequency weighting For the latest Big Data and Business Intelligence tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 932 Packt Video
Lecture 50 — Contextual Text Mining  Contextual Probabilistic Latent Semantic Analysis | UIUC
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Information Retrieval Using Latent Semantic Analysis
 
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Watch at 0.75x for better understanding EE5120 || Applied Linear Algebra Course Project || IIT Madras This video explains the application of Singular Value Decomposition in Latent Semantic Analysis.
Views: 1086 Vicky Gangar
Lecture 48 — Dimensionality Reduction with SVD | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Introduction to Latent Semantic Analysis (1/5)
 
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This video introduces the core concepts in Natural Language Processing and the Unsupervised Learning technique, Latent Semantic Analysis. The purposes and benefits of the technique are discussed. In particular, the video highlights how the technique can aid understanding of latent, or hidden, aspects of a body of documents, in addition to reducing the dimensionality of the original dataset. Download the notebook here: https://files.training.databricks.com/classes/lsa-videos/LatentSemanticAnalysisTwoPoems.dbc Don't have a Databricks Account? Sign up for Community Edition: https://databricks.com/try-databricks This is Part 1 of our Introduction to Latent Semantic Analysis Series: https://www.youtube.com/playlist?list=PLroeQp1c-t3qwyrsq66tBxfR6iX6kSslt Learn more at Databricks Academy! https://databricksacademy.com
Views: 391 Databricks Academy
NLP - Text Preprocessing and Text Classification (using Python)
 
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Hi! My name is Andre and this week, we will focus on text classification problem. Although, the methods that we will overview can be applied to text regression as well, but that will be easier to keep in mind text classification problem. And for the example of such problem, we can take sentiment analysis. That is the problem when you have a text of review as an input, and as an output, you have to produce the class of sentiment. For example, it could be two classes like positive and negative. It could be more fine grained like positive, somewhat positive, neutral, somewhat negative, and negative, and so forth. And the example of positive review is the following. "The hotel is really beautiful. Very nice and helpful service at the front desk." So we read that and we understand that is a positive review. As for the negative review, "We had problems to get the Wi-Fi working. The pool area was occupied with young party animals, so the area wasn't fun for us." So, it's easy for us to read this text and to understand whether it has positive or negative sentiment but for computer that is much more difficult. And we'll first start with text preprocessing. And the first thing we have to ask ourselves, is what is text? You can think of text as a sequence, and it can be a sequence of different things. It can be a sequence of characters, that is a very low level representation of text. You can think of it as a sequence of words or maybe more high level features like, phrases like, "I don't really like", that could be a phrase, or a named entity like, the history of museum or the museum of history. And, it could be like bigger chunks like sentences or paragraphs and so forth. Let's start with words and let's denote what word is. It seems natural to think of a text as a sequence of words and you can think of a word as a meaningful sequence of characters. So, it has some meaning and it is usually like,if we take English language for example,it is usually easy to find the boundaries of words because in English we can split upa sentence by spaces or punctuation and all that is left are words.Let's look at the example,Friends, Romans, Countrymen, lend me your ears;so it has commas,it has a semicolon and it has spaces.And if we split them those,then we will get words that are ready for further analysis like Friends,Romans, Countrymen, and so forth.It could be more difficult in German,because in German, there are compound words which are written without spaces at all.And, the longest word that is still in use is the following,you can see it on the slide and it actually stands forinsurance companies which provide legal protection.So for the analysis of this text,it could be beneficial to split that compound word intoseparate words because every one of them actually makes sense.They're just written in such form that they don't have spaces.The Japanese language is a different story.
Views: 9124 Machine Learning TV
Applying Semantic Analyses to Content-based Recommendation and Document Clustering
 
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This talk will present the results of my research on feature generation techniques for unstructured data sources. We apply Probase, a Web-scale knowledge base developed by Microsoft Research Asia, which is generated from the Bing index, search query logs and other sources, to extract concepts from text. We compare the performance of features generated from Probase and two other forms of semantic analysis, Explicit Semantic Analysis using Wikipedia and Latent Dirichlet Allocation. We evaluate the semantic analysis techniques on two tasks, recommendation using Matchbox, which is a platform for probabilistic recommendations from Microsoft Research Cambridge, and clustering using K-Means.
Views: 811 Microsoft Research
Machine Learning - Text Similarity with Python
 
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Learn Machine Learning https://pythonprogramminglanguage.com/machine-learning/ https://pythonprogramminglanguage.com/machine-learning-tasks/ https://pythonprogramminglanguage.com/bag-of-words/ https://pythonprogramminglanguage.com/bag-of-words-euclidian-distance/ Learn Python? https://pythonprogramminglanguage.com/
Views: 10895 Machine Learning
Minimal Semantic Units in Text Analysis
 
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Speaker: Jake Ryland Williams, Drexel University Presented on December 1, 2017, as part of the 2017 TextXD Conference (https://bids.berkeley.edu/events/textxd-conference) at the Berkeley Institute for Data Science (BIDS) (bids.berkeley.edu).
Multi-label Image Classification with Regional Latent Semantic Dependencies
 
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https://arxiv.org/pdf/1612.01082.pdf
Views: 57 platform ai
Text Processing in R by Tim Hoolihan (5/24/2017)
 
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Tim Hoolihan presents on working with text in R using the following packages: tm, topicmodels, lsa.
Introduction to Text Analytics with R: TF-IDF
 
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TF-IDF includes specific coverage of: • Discussion of how the document-term frequency matrix representation can be improved: – How to deal with documents of unequal lengths. – What to do about terms that are very common across documents. •Introduction of the mighty term frequency-inverse document frequency (TF-IDF) to implement these improvements: -TF for dealing with documents of unequal lengths. -IDF for dealing with terms that appear frequently across documents. • Implementation of TF-IDF using R functions and applying TF-IDF to document-term frequency matrices. • Data cleaning of matrices post TF-IDF weighting/transformation. 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 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/H0hD4l40 Watch the latest video tutorials here: https://hubs.ly/H0hD4lb0 See what our past attendees are saying here: https://hubs.ly/H0hD3R-0 -- 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 830 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: 20755 Data Science Dojo
(Basic) Text Analysis with WORDij
 
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This video shows you how to use WORDij (http://wordij.net) to analyze textual data. I focus a) on word and word pair frequencies, and b) on how to create a semantic network and visualize it using gephi (http://gephi.org).
Views: 3027 Bernhard Rieder
Lecture 26 — Probabilistic Latent Semantic Analysis PLSA - Part 1 | UIUC
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
A Trivial Implementation of LSA using Scikit Learn  (2/5)
 
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This video introduces the steps in a full LSA Pipeline and shows how they can be implemented in Databricks Runtime for Machine Learning using the open-source libraries Scikit-Learn and Pandas. These steps are: - Import Raw Data - Build a Document-Term Matrix - Perform a Singular Value Decomposition on the Document-Term Matrix - Examine the generated Topic-Encoded Data This video uses a trivial list of strings as the body of documents so that you can compare your own intuition to the results of the LSA. After completing the process, we examine two byproducts of the LSA—the dictionary and the encoding matrix—in order to gain an understanding of how the documents are being encoded in topic space. This video introduces the core concepts in Natural Language Processing and the Unsupervised Learning technique, Latent Semantic Analysis. The purposes and benefits of the technique are discussed. In particular, the video highlights how the technique can aid understanding of latent, or hidden, aspects of a body of documents, in addition to reducing the dimensionality of the original dataset. Download the notebook here: https://files.training.databricks.com/classes/lsa-videos/LatentSemanticAnalysisTwoPoems.dbc Don't have a Databricks Account? Sign up for Community Edition: https://databricks.com/try-databricks This is Part 2 of our Introduction to Latent Semantic Analysis Series: https://www.youtube.com/playlist?list=PLroeQp1c-t3qwyrsq66tBxfR6iX6kSslt Learn more at Databricks Academy! https://databricksacademy.com
Views: 201 Databricks Academy
Latent Dirichlet Allocation (LDA) for Topic Modeling
 
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LDA Topic Models is a powerful tool for extracting meaning from text. In this video I talk about the idea behind the LDA itself, why does it work. If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: GitHub - https://github.com/bhattbhavesh91 Medium - https://medium.com/@bhattbhavesh91 #TopicModelling #LDA #NLP #machinelearning #python #datascience
Views: 12284 Bhavesh Bhatt
Naive Bayes algorithm in Machine learning Program | Text Classification python (2018)
 
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We have implemented Text Classification in Python using Naive Bayes Classifier. It explains the text classification algorithm from beginner to pro. For understanding the co behind it, refer: https://www.youtube.com/watch?v=Zt83JnjD8zg Here, we have used 20 Newsgroup dataset to train our model for the classification. Link to download the 20 Newsgroup dataset: http://qwone.com/~jason/20Newsgroups/20news-bydate.tar.gz Packages used here are: 1. sklearn 2. Tfidf Vectorizer 3. Multinomial Naive Bayes Classifier 4. Pipeline 5. Metrics Refer the entire code at: https://github.com/codewrestling/TextClassification/blob/master/Text%20Classification.py For slides, refer: https://github.com/codewrestling/TextClassification/raw/master/Text%20Classification.pdf Follow us on Github for more codes: https://github.com/codewrestling machine learning python beginner,machine learning python basics,machine learning python regression,machine learning game python,machine learning applications python
Views: 12324 Code Wrestling
Introduction to Text Analytics with R: Cosine Similarity
 
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Cosine Similarity includes specific coverage of: – How cosine similarity is used to measure similarity between documents in vector space. – The mathematics behind cosine similarity. – Using cosine similarity in text analytics feature engineering. – Evaluation of the effectiveness of the cosine similarity feature. The data and R code used in this series is available via the public GitHub here About the Series This data science tutorial is an Introduction to Text Analytics with R. 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 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/H0hD5gf0 Watch the latest video tutorials here: https://hubs.ly/H0hD5Pk0 See what our past attendees are saying here: https://hubs.ly/H0hD5hd0 -- 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 830 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: 11980 Data Science Dojo
Text Classification - Natural Language Processing With Python and NLTK p.11
 
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Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label. To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment. 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: 108949 sentdex
Mod-01 Lec-28 PCA; SVD; Towards Latent Semantic Indexing(LSI)
 
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Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 10077 nptelhrd
Lecture 47 — Singular Value Decomposition | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
LDA Topic Models
 
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LDA Topic Models is a powerful tool for extracting meaning from text. In this video I talk about the idea behind the LDA itself, why does it work, what are the free tools and frameworks that can be used, what LDA parameters are tuneable, what do they mean in terms of your specific use case and what to look for when you evaluate it.
Views: 89341 Andrius Knispelis
NLP - Linear Models for Text Sentiment Analysis
 
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In this video, we will talk about first text classification model on top of features that we have described. And let's continue with the sentiment classification. We can actually take the IMDB movie reviews dataset, that you can download, it is freely available. It contains 25,000 positive and 25,000 negative reviews. And how did that dataset appear? You can actually look at IMDB website and you can see that people write reviews there, and they actually also provide the number of stars from one star to ten star. They actually rate the movie and write the review. And if you take all those reviews from IMDB website, you can actually use that as a dataset for text classification because you have a text and you have a number of stars, and you can actually think of stars as sentiment. If we have at least seven stars, you can label it as positive sentiment. If it has at most four stars, that means that is a bad movie for a particular person and that is a negative sentiment. And that's how you get the dataset for sentiment classification for free. It contains at most 30 reviews per movie just to make it less biased for any particular movie. These dataset also provides a 50/50 train test split so that future researchers can use the same split and reproduce their results and enhance the model. For evaluation, you can use accuracy and that actually happens because we have the same number of positive and negative reviews. So our dataset is balanced in terms of the size of the classes so we can evaluate accuracy here. Okay, so let's start with first model. Let's takes features, let's take bag 1-grams with TF-IDF values. And in the result, we will have a matrix of features, 25,000 rows and 75,000 columns, and that is a pretty huge feature matrix. And what is more, it is extremely sparse. If you look at how many 0s are there, then you will see that 99.8% of all values in that matrix are 0s. So that actually applies some restrictions on the models that we can use on top of these features. And the model that is usable for these features is logistic regression, which works like the following. It tries to predict the probability of a review being a positive one given the features that we gave that model for that particular review. And the features that we use, let me remind you, is the vector of TF-IDF values. And what you actually can do is you can find the weight for every feature of that bag of force representation. You can multiply each value, each TF-IDF value by that weight, sum all of that things and pass it through a sigmoid activation function and that's how you get logistic regression model. And it's actually a linear classification model and what's good about that is since it's linear, it can handle sparse data. It's really fast to train and what's more, the weights that we get after the training can be interpreted. And let's look at that sigmoid graph at the bottom of the slide. If you have a linear combination that is close to 0, that means that sigmoid will output 0.5. So the probability of a review being positive is 0.5. So we really don't know whether it's positive or negative. But if that linear combination in the argument of our sigmoid function starts to become more and more positive, so it goes further away from zero. Then you see that the probability of a review being positive actually grows really fast. And that means that if we get the weight of our features that are positive, then those weights will likely correspond to the words that a positive. And if you take negative weights, they will correspond to the words that are negative like disgusting or awful.
Views: 3130 Machine Learning TV
NLTK   Basic Text Analytics
 
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Natural Language Processing (NLP) using NLTK and Python to perform basic text analytics such as Word and Sentense Tokenizing, Parts of Speech POS tagging, extracting Named Entities Video covers: Word and Sentense Tokenizer, Parts of Speech POS tokenizer, Named Entities
Views: 24112 Melvin L
Getting Started with NLP and Deep Learning with Python: Latent Semantic Analysis| packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2tDFRbc]. In this video, we will understand latent semantic analysis with an example • Tokenize and vectorize • Apply an SVD to the Xc matrix • Analyze the top 10 words per topic For the latest Big Data and Business Intelligence tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 254 Packt Video
LDA Algorithm Description
 
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Algorithm description for Latent Dirichlet Allocation - CSC529
Views: 43760 Scott Sullivan
Latent Semantic Analysis    13th Personality
 
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Part 1 of 3 in a series.
Views: 43 Bill Pugsley
Amazon SageMaker’s Built-in Algorithm Webinar Series: Blazing Text
 
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In this webinar which covers the Blazing Text algorithm used by Amazon SageMaker - https://amzn.to/2S1lZWD, Pratap Ramamurthy, AWS Partner Solution Architect, will show you how to use Blazing Text for classification, natural language generation and sentiment analysis on text. Learn more - https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
Views: 934 Amazon Web Services
Machine Learning with Text  - TFIDF Vectorizer MultinomialNB Sklearn (Spam Filtering example Part 2)
 
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#MachineLearningText #NLP #TFIDF #DataScience #ScikitLearn #TextFeatures #DataAnalytics #SpamFilter Correction in video : TFIDF- Term Frequency Inverse Document Frequency. Text cannot be used as an input to ML algorithms, therefore we use certain techniques to extract features from text. TFIDF Vectorizer extracts features based on word count giving less weightage to frequent words and more weigtage to rare words. We then apply the features to Multinomial Naive bayes Classifier to classify Spam/ Non Spam messages. For dataset and Ipython Notebooks. GitHub: https://github.com/shreyans29/thesemicolon Support us on Patreon : https://www.patreon.com/thesemicolon Facebook: https://www.facebook.com/thesemicolon.code/ Check out the machine learning, deep learning and developer products USA: https://www.amazon.com/shop/thesemicolon India: https://www.amazon.in/shop/thesemicolon
Views: 25851 The Semicolon
Text Mining (part 6) -  Cleaning Corpus text in R
 
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Clean multiple documents of unnecessary words, punctuation, digits, etc.
Views: 8675 Jalayer Academy
Intro to Text Mining Sentiment Analysis using R-12th March 2016
 
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Analytics Accelerator Program, February 2016-April 2016 batch
Views: 26075 Equiskill Insights LLP
Text Mining - Classification in RapidMiner
 
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This video shows how to perform simple text classification in RapidMiner. Based upon a manually coded 10 airline comments we created a model to predict whether the comment is about a specific feature, such as seat, service or schedule.
Views: 449 Sonya Zhang
Jurgen Van Gael - Hierarchical Text Classification using Python (and friends)
 
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PyData London 2014 In this talk I will describe a system that we've built for doing hierarchical text classification. I will describe the logical setup of the various steps involved: data processing, feature selection, training, validation and labelling. To make this all work in practice we've mapped the setup onto a Hadoop cluster. I'll discuss some of the pro's and con's that we've run into when working with Python and Hadoop. Finally, I'll discuss how we use crowdsourcing to continuously improve the quality of our hierarchical classifier.
Views: 9269 PyData
What is LATENT SEMANTIC MAPPING? What does LATENT SEMANTIC MAPPING mean?
 
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What is LATENT SEMANTIC MAPPING? What does LATENT SEMANTIC MAPPING mean? LATENT SEMANTIC MAPPING meaning - LATENT SEMANTIC MAPPING definition - LATENT SEMANTIC MAPPING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Latent semantic mapping (LSM) is a data-driven framework to model globally meaningful relationships implicit in large volumes of (often textual) data. It is a generalization of latent semantic analysis. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. LSM was derived from earlier work on latent semantic analysis. There are 3 main characteristics of latent semantic analysis: Discrete entities, usually in the form of words and documents, are mapped onto continuous vectors, the mapping involves a form of global correlation pattern, and dimensionality reduction is an important aspect of the analysis process. These constitute generic properties, and have been identified as potentially useful in a variety of different contexts. This usefulness has encouraged great interest in LSM. The intended product of latent semantic mapping, is a data-driven framework for modeling relationships in large volumes of data. Mac OS X v10.5 and later includes a framework implementing latent semantic mapping.
Views: 217 The Audiopedia
Latent semantic analysis
 
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Latent semantic analysis is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text. A matrix containing word counts per paragraph is constructed from a large piece of text and a mathematical technique called singular value decomposition is used to reduce the number of rows while preserving the similarity structure among columns. Words are then compared by taking the cosine of the angle between the two vectors formed by any two rows. Values close to 1 represent very similar words while values close to 0 represent very dissimilar words. An information retrieval method using latent semantic structure was patented in 1988 by Scott Deerwester, Susan Dumais, George Furnas, Richard Harshman, Thomas Landauer, Karen Lochbaum and Lynn Streeter. In the context of its application to information retrieval, it is sometimes called Latent Semantic Indexing. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 7294 Audiopedia
Tariq Rashid - Dimension Reduction and Extracting Topics - A Gentle Introduction
 
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Filmed at PyData 2017 Description Text mining has many powerful methods for unlocking insights into the messy, ambiguous, but interesting text created by people. Singular value decomposition (SVD) is a useful method for reducing the many dimensions of text data, and distill out key themes in that text - called topic modelling or latent semantic analysis. This talk for beginners will gently explain SVD and how to use it. Abstract Text mining and natural language processing are hugely powerful fields that can unlock insights into the vast amounts of human knowledge, creativity and drivel (!) for automated computing. Examples include the fun of highlighting trends in internet chatter through to more serious analysis of finding patterns and links in leaked data sets of public interest. One key tool is to reduce the many dimensions of text data, and distill out the key themes in that text. People call this topic modelling, latent semantic analysis, and a few other names too. The powerful method at the heart of this is called singular value decomposition (SVD). This talk will gently introduce singular valued decomposition (SVD), explaining the mathematics in an accessible manner, and demonstrate how it can be used, using the Chilcot Iraq Report as an example dataset. Example code, notebooks and data sets are public on GitHub, and there is a blog for more discussion of this, and other text mining ideas http://makeyourowntextminingtoolkit.blogspot.co.uk 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. We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1137 PyData
Words as Features for Learning - Natural Language Processing With Python and NLTK p.12
 
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For our text classification, we have to find some way to "describe" bits of data, which are labeled as either positive or negative for machine learning training purposes. These descriptions are called "features" in machine learning. For our project, we're just going to simply classify each word within a positive or negative review as a "feature" of that review. Then, as we go on, we can train a classifier by showing it all of the features of positive and negative reviews (all the words), and let it try to figure out the more meaningful differences between a positive review and a negative review, by simply looking for common negative review words and common positive review words. 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: 71359 sentdex
[Text Classification - NLP] 6 Distributional Semantic Model
 
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คลิปสำหรับวิชา Computational Linguistics คณะอักษรศาสตร์ จุฬาลงกรณ์มหาวิทยาลัย ปี 2561 โดย อ. ดร.อรรถพล ธำรงรัตนฤทธิ์ เว็บไซต์ของวิชาและแบบฝึกหัด attapol.github.io/compling
Text Mining (part 2)  -  Cleaning Text Data in R (single document)
 
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Clean Text of punctuation, digits, stopwords, whitespace, and lowercase.
Views: 21953 Jalayer Academy