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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: 39225 DeepLearning.TV
What is TEXT CORPUS? What does TEXT CORPUS mean? TEXT CORPUS meaning, definition & explanation
 
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What is TEXT CORPUS? What does TEXT CORPUS mean? TEXT CORPUS meaning - TEXT CORPUS definition - TEXT CORPUS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In linguistics, a corpus (plural corpora) or text corpus is a large and structured set of texts (nowadays usually electronically stored and processed). They are used to do statistical analysis and hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory. A corpus may contain texts in a single language (monolingual corpus) or text data in multiple languages (multilingual corpus). Multilingual corpora that have been specially formatted for side-by-side comparison are called aligned parallel corpora. There are two main types of parallel corpora which contain texts in two languages. In a translation corpus, the texts in one language are translations of texts in the other language. In a comparable corpus, the texts are of the same kind and cover the same content, but they are not translations of each other. To exploit a parallel text, some kind of text alignment identifying equivalent text segments (phrases or sentences) is a prerequisite for analysis. Machine translation algorithms for translating between two languages are often trained using parallel fragments comprising a first language corpus and a second language corpus which is an element-for-element translation of the first language corpus. In order to make the corpora more useful for doing linguistic research, they are often subjected to a process known as annotation. An example of annotating a corpus is part-of-speech tagging, or POS-tagging, in which information about each word's part of speech (verb, noun, adjective, etc.) is added to the corpus in the form of tags. Another example is indicating the lemma (base) form of each word. When the language of the corpus is not a working language of the researchers who use it, interlinear glossing is used to make the annotation bilingual. Some corpora have further structured levels of analysis applied. In particular, a number of smaller corpora may be fully parsed. Such corpora are usually called Treebanks or Parsed Corpora. The difficulty of ensuring that the entire corpus is completely and consistently annotated means that these corpora are usually smaller, containing around one to three million words. Other levels of linguistic structured analysis are possible, including annotations for morphology, semantics and pragmatics. Corpora are the main knowledge base in corpus linguistics. The analysis and processing of various types of corpora are also the subject of much work in computational linguistics, speech recognition and machine translation, where they are often used to create hidden Markov models for part of speech tagging and other purposes. Corpora and frequency lists derived from them are useful for language teaching. Corpora can be considered as a type of foreign language writing aid as the contextualised grammatical knowledge acquired by non-native language users through exposure to authentic texts in corpora allows learners to grasp the manner of sentence formation in the target language, enabling effective writing. Text corpora are also used in the study of historical documents, for example in attempts to decipher ancient scripts, or in Biblical scholarship. Some archaeological corpora can be of such short duration that they provide a snapshot in time. One of the shortest corpora in time, may be the 15–30 year Amarna letters texts (1350 BC). The corpus of an ancient city, (for example the "Kültepe Texts" of Turkey), may go through a series of corpora, determined by their find site dates.
Views: 1038 The Audiopedia
R tutorial: Cleaning and preprocessing text
 
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Learn more about text mining with R: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words Now that you have a corpus, you have to take it from the unorganized raw state and start to clean it up. We will focus on some common preprocessing functions. But before we actually apply them to the corpus, let’s learn what each one does because you don’t always apply the same ones for all your analyses. Base R has a function tolower. It makes all the characters in a string lowercase. This is helpful for term aggregation but can be harmful if you are trying to identify proper nouns like cities. The removePunctuation function...well it removes punctuation. This can be especially helpful in social media but can be harmful if you are trying to find emoticons made of punctuation marks like a smiley face. Depending on your analysis you may want to remove numbers. Obviously don’t do this if you are trying to text mine quantities or currency amounts but removeNumbers may be useful sometimes. The stripWhitespace function is also very useful. Sometimes text has extra tabbed whitespace or extra lines. This simply removes it. A very important function from tm is removeWords. You can probably guess that a lot of words like "the" and "of" are not very interesting, so may need to be removed. All of these transformations are applied to the corpus using the tm_map function. This text mining function is an interface to transform your corpus through a mapping to the corpus content. You see here the tm_map takes a corpus, then one of the preprocessing functions like removeNumbers or removePunctuation to transform the corpus. If the transforming function is not from the tm library it has to be wrapped in the content_transformer function. Doing this tells tm_map to import the function and use it on the content of the corpus. The stemDocument function uses an algorithm to segment words to their base. In this example, you can see "complicatedly", "complicated" and "complication" all get stemmed to "complic". This definitely helps aggregate terms. The problem is that you are often left with tokens that are not words! So you have to take an additional step to complete the base tokens. The stemCompletion function takes as arguments the stemmed words and a dictionary of complete words. In this example, the dictionary is only "complicate", but you can see how all three words were unified to "complicate". You can even use a corpus as your completion dictionary as shown here. There is another whole group of preprocessing functions from the qdap package which can complement these nicely. In the exercises, you will have the opportunity to work with both tm and qdap preprocessing functions, then apply them to a corpus.
Views: 15674 DataCamp
What is Text Mining?
 
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An introduction to the basics of text and data mining. To learn more about text mining, view the video "How does Text Mining Work?" here: https://youtu.be/xxqrIZyKKuk
Views: 40612 Elsevier
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: 368034 sentdex
How to build a corpus (text formats)
 
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A brief description of how to handle different text formats when building a corpus in corpus linguistics. Feel free to use in your own teaching of corpus linguistics.
Views: 7340 CorpusLingAnalysis
Exploratory analysis of word frequencies across corpus texts
 
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http://www.birmingham.ac.uk/cl2017 Dr Andrew Hardie (Lancaster University) delivers the opening plenary at the Corpus Linguistics Conference 2017 at the University of Birmingham.
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 156538 Timothy DAuria
How to process text files with RapidMiner
 
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In this video I process transcriptions from Hugo Chavez's TV programme "Alo Presidente" to find patterns in his speech. Watching this video you will learn how to: -Download several documents at once from a webpage using a Firefox plugin. - Batch convert pdf files to text using a very simple script and a java application. - Process documents with Rapid Miner using their association rules feature to find patterns in them.
Views: 33952 Alba Madriz
Text Mining (part 7) -  Comparison Wordcloud in R
 
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Create a Wordcloud and Comparison Wordcloud for your Corpus. Create a Term Document Matrix in the process.
Views: 5999 Jalayer Academy
Information Extraction and Text Mining from Large Document Corpora
 
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Social Analytics and Text Mining, Lecture of Prof. Prasenjit Mitra, College of Information Sciences and Technology, Pennsylvania State University, "Information Extraction and Text Mining from Large Document Corpora" Data Mining for Business Intelligence - Bridging the Gap Ben-Gurion University of the Negev
Views: 2880 BenGurionUniversity
Build a corpus from your own texts/data
 
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Learn to build a corpus from your own texts and data which you upload to Sketch Engine to receive an annotated (pos-tagged) and lemmatized corpus in many languages.
Views: 237 Sketch Engine
Christopher Zorn, "Corpus-Based Dictionaries for Sentiment Analysis of Specialized Vocabularies"
 
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International Methods Colloquium talk, February 20th 2015.
Views: 557 Methods Colloquium
NLTK Corpora - Natural Language Processing With Python and NLTK p.9
 
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Remember from the beginning, we talked about this term, "corpora." Again, corpora is just a body of texts. Generally, corpora are grouped by some sort of defining characteristic. NLTK is a massive toolkit for you. part of what they give you is a ton of highly valuable corpora to learn with, train against, and some of them are even capable of using in production. This video is going to be all about accessing your corpora! sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 45647 sentdex
Text Corpus Analysis - PoolParty Tutorial #21
 
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The latest version 4 of PoolParty Thesaurus Server (http://www.poolparty.biz/) offers a full-blown text corpus analysis module. By using this, taxonomists and thesaurus managers can analyze large text collections and identify gaps between a thesaurus and the content base. One can glean candidate terms and can identify parts of the taxonomy which do not occur in the actual content. PoolParty's text mining capabilities are outstanding: highly performant and precise, multilingual and to be used in various industries.
Weka Text Classification for First Time & Beginner Users
 
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59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 129140 Brandon Weinberg
Sentiment Analysis in 4 Minutes
 
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Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Views: 82385 Siraj Raval
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/
Views: 127370 Siraj Raval
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: 13078 Jalayer Academy
What is Text Mining?
 
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The introduction of Text Mining-- Created using PowToon -- Free sign up at http://www.powtoon.com/join -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Views: 2882 Jian Cui
Arabic Processing & Text Mining by Dr. AlMuhtaseb
 
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SDMA 2014 Dr. Husni AlMuhtaseb - Assistant professor from King Fahd University of Petroleum and Mineral (KFUPM)
Views: 287 Megdam Center
GraphSearch & Text Corpus Analysis - PoolParty 5.2
 
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See how PoolParty's taxonomy management methodology (https://www.poolparty.biz) is now supported even more efficiently by PoolParty's latest release. We demonstrate how PoolParty 5.2 makes use of deep text mining including corpus analysis and co-occurrence analysis. We show an example based on UNESCO world heritage sites and demonstrate how automatic classification can be extended step-by-step. An immediate feedback is given by PoolParty's faceted GraphSearch. Initial taxonomies can be built by using PoolParty's linked data harvester to fetch data from DBpedia.
KEYNESS IN CORPUS ANALYSIS
 
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This short lecture is about the notion of keyness in comparing two corpora. Reference: Routledge Handbook of Corpus Linguistics (2010)
Views: 410 Afida Mohamad Ali
Corpus Linguistics: Method Analysis Interpretation
 
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Tony McEnery Has been working for over 20 years to help pioneer new ways to use computers to analyse very large collections of language data. LOCATION LANCASTER UNIVERSITY, UK
Views: 33 Zhigang Bai
What does GATE do
 
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This is a example in GATE which shows the results of the default ANNIE pipeline on an English document. In this case the document is "That's what she said" that lovely catch phrase from Michael Scott in The Office TV show http://www.cs.washington.edu/homes/brun/pubs/pubs/Kiddon11.pdf it discusses humor recognition...
Views: 27585 cesine0
Text Mining (part 3)  -  Sentiment Analysis and Wordcloud in R (single document)
 
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Sentiment Analysis Implementation and Wordcloud. Find the terms here: http://ptrckprry.com/course/ssd/data/positive-words.txt http://ptrckprry.com/course/ssd/data/negative-words.txt
Views: 17655 Jalayer Academy
EmoText for opinion mining in long texts
 
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http://socioware.de https://www.researchgate.net/publication/278383087_Opinion_Mining_and_Lexical_Affect_Sensing EmoText for opinion mining in long texts illustrates a domain-independent approach to opinion mining. A thorough description is available in the book "Opinion mining and lexical affect sensing". Empirically revealed that texts should contain not less than 200 words for reliable classification. The engine evaluates features (lexical, stylometric, grammatical, deictic) using different evaluation methods and uses the SMO or NaiveBayes classifiers from the WEKA data mining toolkit for text classification. Statistical EmoText formed a basis for the statistical framework for experimentation and rapid prototyping. The approach was tested on the following English corpora: a Pang corpus with weblogs, Berardinelli movie review corpus with movie reviews, a corpus with spontaneous dialogues (the SAL corpus), and a corpus with product reviews.
Views: 959 Alexander Osherenko
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: 85017 sentdex
Corpus Annotation
 
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Views: 246 CLAMP Toolkit
Natural Language Processing with Graphs
 
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William Lyon, Developer Relations Enginner, Neo4j:During this webinar, we’ll provide an overview of graph databases, followed by a survey of the role for graph databases in natural language processing tasks, including: modeling text as a graph, mining word associations from a text corpus using a graph data model, and mining opinions from a corpus of product reviews. We'll conclude with a demonstration of how graphs can enable content recommendation based on keyword extraction.
Views: 28908 Neo4j
Creating a corpus by uploading your data.
 
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There are many ways in which you can create a corpus using the Sketch Engine. In this video you will see how you can create a corpus by uploading data files to the Sketch Engine. www.sketchengine.co.uk
Views: 9100 TheSketchEngine
Open and Exploratory Extraction of Relations and Common Sense from Large Text Corpora - Alan Akbik
 
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Alan Akbik November 10, 2014 Title: Open and Exploratory Extraction of Relations (and Common Sense) from Large Text Corpora Abstract: The use of deep syntactic information such as typed dependencies has been shown to be very effective in Information Extraction (IE). Despite this potential, the process of manually creating rule-based information extractors that operate on dependency trees is not intuitive for persons without an extensive NLP background. In this talk, I present an approach and a graphical tool that allows even novice users to quickly and easily define extraction patterns over dependency trees and directly execute them on a very large text corpus. This enables users to explore a corpus for structured information of interest in a highly interactive and data-guided fashion, and allows them to create extractors for those semantic relations they find interesting. I then present a project in which we use Information Extraction to automatically construct a very large common sense knowledge base. This knowledge base - dubbed "The Weltmodell" - contains common sense facts that pertain to proper noun concepts; an example of this is the concept "coffee", for which we know that it is typically drunk by a person or brought by a waiter. I show how we mine such information from very large amounts of text, how we quantify notions such as typicality and similarity, and discuss some ideas how such world knowledge can be used to address reasoning tasks.
TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling
 
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This video demonstrates the features of the TopicNets system with some concrete examples.
Views: 2653 brynjargr
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: 61478 deltaDNA
O'Reilly Webcast: How to Develop Language Annotations for Machine Learning Algorithms
 
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Text-based data mining and information extraction systems that make use of machine learning techniques require annotated datasets for training the algorithms. In this webcast presented by James Pustejovsky and Amber Stubbs, we will discuss the steps involved in creating your own training corpus for such machine learning algorithms. We walk you through: The annotation cycle Selecting an annotation task Creating the annotation specification Designing the guidelines Creating a "gold standard" corpus Beginning the actual data creation with the annotation process We then mention the most relevant machine learning algorithms for natural language data and tasks, and provide hints for how to choose the right one for your learning task and your own dataset. Finally, we discuss testing and evaluation of the algorithm, along with suggestions for how to revise your system depending on the resulting performance. This is a unique, up-close, step-by-step look at the entire development cycle for NLP system design, from your initial idea, to spec, through annotation and corpus development, to training and testing your algorithm. Don't miss this informative webcast. About James Pustejovsky James Pustejovsky holds the TJX/Felberg Chair in Computer Science at Brandeis University, where he directs the Lab for Linguistics and Computation, and chairs both the Program in Language and Linguistics and the Computational Linguistics MA Program. He has conducted research in computational linguistics, AI, lexical semantics, temporal reasoning, and corpus linguistics and language annotation. He is currently head of a working group within ISO/TC37/SC4 to develop a Semantic Annotation Framework, and is the author of the recently approved ISO specification for time annotation (SemAF-Time, ISO-TimeML) and the draft specification for space annotation (SemAF-Space, ISO-Space). Pustejovsky was PI of a large NSF-funded effort, "Towards a Comprehensive Linguistic Annotation of Language," that involved merging several diverse linguistic annotations (PropBank, NomBank, the Discourse Treebank, TimeBank, and Opinion Corpus) into a unified representation. Currently, he is Co-PI of a major project funded by the NSF to address interoperability for NLP data and tools. He has taught computational linguistics to both graduates and undergraduates for 20 years, and corpus linguistics for eight years. http://twitter.com/jamespusto About Amber Stubbs Amber Stubbs recently completed her Ph.D. in Computer Science at Brandeis University, and is currently a Postdoctoral Associate at SUNY Albany. Her dissertation focused on creating an annotation methodology to aid in extracting high-level information from natural language files, particularly biomedical texts. Her website can be found at http://pages.cs.brandeis.edu/~astubbs/ Produced by: Yasmina Greco
Views: 2717 O'Reilly
Statistical Text Analysis for Social Science
 
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What can text analysis tell us about society? Corpora of news, books, and social media encode human beliefs and culture. But it is impossible for a researcher to read all of today's rapidly growing text archives. My research develops statistical text analysis methods that measure social phenomena from textual content, especially in news and social media data. For example: How do changes to public opinion appear in microblogs? What topics get censored in the Chinese Internet? What character archetypes recur in movie plots? How do geography and ethnicity affect the diffusion of new language? In order to answer these questions effectively, we must apply and develop scientific methods in statistics, computation, and linguistics. In this talk I will illustrate these methods in a project that analyzes events in international politics. Political scientists are interested in studying international relations through *event data*: time series records of who did what to whom, as described in news articles. To address this event extraction problem, we develop an unsupervised Bayesian model of semantic event classes, which learns the verbs and textual descriptions that correspond to types of diplomatic and military interactions between countries. The model uses dynamic logistic normal priors to drive the learning of semantic classes; but unlike a topic model, it leverages deeper linguistic analysis of syntactic argument structure. Using a corpus of several million news articles over 15 years, we quantitatively evaluate how well its event types match ones defined by experts in previous work, and how well its inferences about countries correspond to real-world conflict. The method also supports exploratory analysis; for example, of the recent history of Israeli-Palestinian relations.
Views: 839 Microsoft Research
Agile Text Mining for Knowledge Discovery - David Milward (Linguamatics)
 
05:33
Agile Text Mining for Knowledge Discovery
Views: 373 ChemAxon
Training Data - Data Conversion and Corpus Preparation
 
11:28
This presentation and screencast describes the required training data format for the Moses SMT system and shows how to convert data into this format. It also shows how to align text from translated documents and how to convert TMX files to source more data for SMT training.
Views: 4672 TAUS Videos
Unboxing Six Open Source Annotation Tools - episode C01
 
17:20
Think of this as an unboxing video for annotation software - this is the first time I've tried running any of this software. Don't expect any good demos, I'm just showing you where to find them along with some resources. GATE https://gate.ac.uk/family/ MAE2 https://keighrim.github.io/mae-annotation/ BRAT http://brat.nlplab.org/features.html WebAnno https://webanno.github.io/webanno/ Annis http://corpus-tools.org/annis/ SLATE https://bitbucket.org/dainkaplan/slate/ Works cited: Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications https://smile.amazon.com/Natural-Language-Annotation-Machine-Learning/dp/1449306667/ Overview of Annotation Creation: Processes & Tools. Finlayson, Mark & Erjavec, Tomaž. (2016). https://www.researchgate.net/publication/301847215_Overview_of_Annotation_Creation_Processes_Tools Handbook of Linguistic Annotation. "Collaborative Web-Based Tools for Multi-layer Text Annotation" pp 229-256 https://link.springer.com/chapter/10.1007/978-94-024-0881-2_8 Also, this is the document I meant to show at 14:21 in the video: Annotation Process Management Revisited Dain Kaplan, Ryu Iida, Takenobu Tokunaga Department of Computer Science, Tokyo Institute of Technology http://www.lrec-conf.org/proceedings/lrec2010/pdf/129_Paper.pdf
Views: 946 Norman Gilmore
NLTK Text Processing 09 - Bigrams
 
09:20
In this video, I talk about Bigram Collocations. Bigrams in NLTK by Rocky DeRaze
Views: 2969 Rocky DeRaze
Chinese Natural Language Processing
 
07:43
Text mining is one of the prospering areas in data science that allows data scientist to work with textual contents – however, some common practices around text mining, such as stopwords and stemming, are not applicable to Chinese texts due to the difference in language structures. On the other hand, a study from InternetWorld Stats showed that Chinese Language Internet users accounted for 23.2% of the World Internet users (as of December 31, 2013), which is the second largest group of users (native English users if the largest group at 28.6%). No doubt that the business world has a strong demand on text-mining skills for Chinese texts. It is important to provide knowledge and necessary tools to extend data scientist text-mining capacity to include Chinese text contents. Follow us on: https://www.facebook.com/experfy https://twitter.com/experfy https://experfy.com
Views: 872 Experfy
Corpus data
 
06:14
CEAL present Corpus data by John D. Bunting, Georgia State University.
Views: 283 UPNAjusco
text mining, web mining and sentiment analysis
 
13:28
text mining, web mining
Views: 1415 Kakoli Bandyopadhyay
Text mining
 
00:47
Text mining application helps in automated data entry. The operator enters free text and the software recognize the sentences and words. The results are exported to the database or other format.
Views: 91 Sylwester Madej
Multilingual Text Mining: Lost in Translation, Found in Native Language Mining - Rohini Srihari
 
35:16
There has been a meteoric rise in the amount of multilingual content on the web. This is primarily due to social media sites such as Facebook, and Twitter, as well as blogs, discussion forums, and reader responses to articles on traditional news sites. Language usage statistics indicate that Chinese is a very close second to English, and could overtake it to become the dominant language on the web. It is also interesting to see the explosive growth in languages such as Arabic. The availability of this content warrants a discussion on how such information can be effectively utilized. Such data can be mined for many purposes including business-related competitive insight, e-commerce, as well as citizen response to current issues. This talk will begin with motivations for multilingual text mining, including commercial and societal applications, digital humanities applications such as semi-automated curation of online discussion forums, and lastly, government applications, where the value proposition (benefits, costs and value) is different, but equally compelling. There are several issues to be touched upon, beginning with the need for processing native language, as opposed to using machine translated text. In tasks such as sentiment or behaviour analysis, it can certainly be argued that a lot is lost in translation, since these depend on subtle nuances in language usage. On the other hand, processing native language is challenging, since it requires a multitude of linguistic resources such as lexicons, grammars, translation dictionaries, and annotated data. This is especially true for "resourceMpoor languages" such as Urdu, and Somali, languages spoken in parts of the world where there is considerable focus nowadays. The availability of content such as multilingual Wikipedia provides an opportunity to automatically generate needed resources, and explore alternate techniques for language processing. The rise of multilingual social media also leads to interesting developments such as code mixing, and code switching giving birth to "new" languages such as Hinglish, Urdish and Spanglish! This phenomena exhibits both pros and cons, in addition to posing difficult challenges to automatic natural language processing. But there is also an opportunity to use crowd-sourcing to preserve languages and dialects that are gradually becoming extinct. It is worthwhile to explore frameworks for facilitating such efforts, which are currently very ad hoc. In summary, the availability of multilingual data provides new opportunities in a variety of applications, and effective mining could lead to better cross-cultural communication. Questions Addressed (i) Motivation for mining multilingual text. (ii) The need for processing native language (vs. machine translated text). (iii) Multilingual Social Media: challenges and opportunities, e.g., preserving languages and dialects.
Views: 1383 UA German Department