Search results “Sparsity in text mining tokenizing”
Feature Extraction from Text (USING PYTHON)
Hi. In this lecture will transform tokens into features. And the best way to do that is Bag of Words. Let's count occurrences of a particular token in our text. The motivation is the following. We're actually looking for marker words like excellent or disappointed, and we want to detect those words, and make decisions based on absence or presence of that particular word, and how it might work. Let's take an example of three reviews like a good movie, not a good movie, did not like. Let's take all the possible words or tokens that we have in our documents. And for each such token, let's introduce a new feature or column that will correspond to that particular word. So, that is a pretty huge metrics of numbers, and how we translate our text into a vector in that metrics or row in that metrics. So, let's take for example good movie review. We have the word good, which is present in our text. So we put one in the column that corresponds to that word, then comes word movie, and we put one in the second column just to show that that word is actually seen in our text. We don't have any other words, so all the rest are zeroes. And that is a really long vector which is sparse in a sense that it has a lot of zeroes. And for not a good movie, it will have four ones, and all the rest of zeroes and so forth. This process is called text vectorization, because we actually replace the text with a huge vector of numbers, and each dimension of that vector corresponds to a certain token in our database. You can actually see that it has some problems. The first one is that we lose word order, because we can actually shuffle over words, and the representation on the right will stay the same. And that's why it's called bag of words, because it's a bag they're not ordered, and so they can come up in any order. And different problem is that counters are not normalized. Let's solve these two problems, and let's start with preserving some ordering. So how can we do that? Actually you can easily come to an idea that you should look at token pairs, triplets, or different combinations. These approach is also called as extracting n-grams. One gram stands for tokens, two gram stands for a token pair and so forth. So let's look how it might work. We have the same three reviews, and now we don't only have columns that correspond to tokens, but we have also columns that correspond to let's say token pairs. And our good movie review now translates into vector, which has one in a column corresponding to that token pair good movie, for movie for good and so forth. So, this way, we preserve some local word order, and we hope that that will help us to analyze this text better. The problems are obvious though. This representation can have too many features, because let's say you have 100,000 words in your database, and if you try to take the pairs of those words, then you can actually come up with a huge number that can exponentially grow with the number of consecutive words that you want to analyze. So that is a problem. And to overcome that problem, we can actually remove some n-grams. Let's remove n-grams from features based on their occurrence frequency in documents of our corpus. You can actually see that for high frequency n-grams, as well as for low frequency n-grams, we can show why we don't need those n-grams. For high frequency, if you take a text and take high frequency n-grams that is seen in almost all of the documents, and for English language that would be articles, and preposition, and stuff like that. Because they're just there for grammatical structure and they don't have much meaning. These are called stop-words, they won't help us to discriminate texts, and we can pretty easily remove them. Another story is low frequency n-grams, and if you look at low frequency n-grams, you actually find typos because people type with mistakes, or rare n-grams that's usually not seen in any other reviews. And both of them are bad for our model, because if we don't remove these tokens, then very likely we will overfeed, because that would be a very good feature for our future classifier that can just see that, okay, we have a review that has a typo, and we had only like two of those reviews, which had those typo, and it's pretty clear whether it's positive or negative. So, it can learn some independences that are actually not there and we don't really need them. And the last one is medium frequency n-grams, and those are really good n-grams, because they contain n-grams that are not stop-words, that are not typos and we actually look at them. And, the problem is there're a lot of medium frequency n-grams. And it proved to be useful to look at n-gram frequency in our corpus for filtering out bad n-grams. What if we can use the same frequency for ranking of medium frequency n-grams?
Views: 5189 Machine Learning TV
NLP - Linear Models for Text Sentiment Analysis
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: 1596 Machine Learning TV
R tutorial: Cleaning and preprocessing text
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: 19635 DataCamp
St. Louis University - Text Mining Research Group: TextMining.jl
Visit http://julialang.org/ to download Julia.
Views: 1425 The Julia Language
Simple Deep Neural Networks for Text Classification
Hi. In this video, we will apply neural networks for text. And let's first remember, what is text? You can think of it as a sequence of characters, words or anything else. And in this video, we will continue to think of text as a sequence of words or tokens. And let's remember how bag of words works. You have every word and forever distinct word that you have in your dataset, you have a feature column. And you actually effectively vectorizing each word with one-hot-encoded vector that is a huge vector of zeros that has only one non-zero value which is in the column corresponding to that particular word. So in this example, we have very, good, and movie, and all of them are vectorized independently. And in this setting, you actually for real world problems, you have like hundreds of thousands of columns. And how do we get to bag of words representation? You can actually see that we can sum up all those values, all those vectors, and we come up with a bag of words vectorization that now corresponds to very, good, movie. And so, it could be good to think about bag of words representation as a sum of sparse one-hot-encoded vectors corresponding to each particular word. Okay, let's move to neural network way. And opposite to the sparse way that we've seen in bag of words, in neural networks, we usually like dense representation. And that means that we can replace each word by a dense vector that is much shorter. It can have 300 values, and now it has any real valued items in those vectors. And an example of such vectors is word2vec embeddings, that are pretrained embeddings that are done in an unsupervised manner. And we will actually dive into details on word2vec in the next two weeks. But, all we have to know right now is that, word2vec vectors have a nice property. Words that have similar context in terms of neighboring words, they tend to have vectors that are collinear, that actually point to roughly the same direction. And that is a very nice property that we will further use. Okay, so, now we can replace each word with a dense vector of 300 real values. What do we do next? How can we come up with a feature descriptor for the whole text? Actually, we can use the same manner as we used for bag of words. We can just dig the sum of those vectors and we have a representation based on word2vec embeddings for the whole text, like very good movie. And, that's some of word2vec vectors actually works in practice. It can give you a great baseline descriptor, a baseline features for your classifier and that can actually work pretty well. Another approach is doing a neural network over these embeddings.
Views: 9123 Machine Learning TV
FAQ #1: Tips & tricks for NLP, annotation & training with Prodigy and spaCy
Prodigy is an annotation tool for creating training data for machine learning models. In this video, I'll be talking about a few frequently asked questions and share some general tips and tricks for how to structure your NLP annotation projects, how to design your label schemes and how to solve common problems. PRODIGY ● Website: https://prodi.gy ● Forum: https://support.prodi.gy ● Recipes repo: https://github.com/explosion/prodigy-recipes THIS VIDEO [0:46] Binary of manual annotation? ● ner.teach vs. ner.match https://support.prodi.gy/t/877 ● Best practices for validation sets https://support.prodi.gy/t/693 [3:34] Accept or reject partial suggestions? ● How to score incompletely highlighted entities https://support.prodi.gy/t/625 ● Should I reject or accept partially correct predictions? https://support.prodi.gy/t/945 [5:35] Reject example or skip it? ● Reject or skip examples for text classifier annotations https://support.prodi.gy/t/998 ● Ignored sentences for text classification https://support.prodi.gy/t/1183 [7:30] What if I need to label long texts? Dealing with sparse data https://support.prodi.gy/t/518 Text categorization at document level https://support.prodi.gy/t/1160 [9:24] Fine-tune pre-trained model or start from scratch? ● Pre-trained model vs training a model from scratch https://support.prodi.gy/t/631/4 ● Fact extraction for earnings news https://support.prodi.gy/t/1023 ● Extracting current and prior company affiliations from bios https://support.prodi.gy/t/1176 ● NER or PhraseMatcher https://support.prodi.gy/t/686 FOLLOW US ● Explosion AI: https://twitter.com/explosion_ai ● Ines Montani: https://twitter.com/_inesmontani ● Matthew Honnibal: https://twitter.com/honnibal
Views: 1096 Explosion AI
Laws of Text 2: Zipf's Law
If you rank the words by their frequency in a text corpus, rank times the frequency will be approximately constant. This is known as Zipf's law.
Views: 13538 Victor Lavrenko
Clustering + Feature Extraction on Text with H2O and Lexalytics - Seth Redmore
Seth Redmore, Chief Marketing Officer at Lexalytics, Inc. H2O World 2015, Day 3 Contribute to H2O open source machine learning software https://github.com/h2oai Check out more slides on open source machine learning software at: http://www.slideshare.net/0xdata
Views: 920 H2O.ai
Wenbo Wang: Automatic Emotion Identification from Text
Title: Automatic Emotion Identification from Text Slides: http://www.slideshare.net/knoesis/wenbo-wang-dissertation-defense-knoesis-wright-state-university Abstract: People’s emotions can be gleaned from their text using machine learning techniques to build models that exploit large self-labeled emotion data from social media. Further, the self-labeled emotion data can be effectively adapted to train emotion classifiers in different target domains where training data are sparse. Emotions are both prevalent in and essential to most aspects of our lives. They influence our decision-making, affect our social relationships and shape our daily behavior. With the rapid growth of emotion-rich textual content, such as microblog posts, blog posts, and forum discussions, there is a growing need to develop algorithms and techniques for identifying people's emotions expressed in text. It has valuable implications for the studies of suicide prevention, employee productivity, well-being of people, customer relationship management, etc. However, emotion identification is quite challenging partly due to the following reasons: i) It is a multi-class classification problem that usually involves at least six basic emotions. Text describing an event or situation that causes the emotion can be devoid of explicit emotion-bearing words, thus the distinction between different emotions can be very subtle, which makes it difficult to glean emotions purely by keywords. ii) Manual annotation of emotion data by human experts is very labor-intensive and error-prone. iii) Existing labeled emotion datasets are relatively small, which fails to provide a comprehensive coverage of emotion-triggering events and situations. This dissertation aims at understanding the emotion identification problem and developing general techniques to tackle the above challenges. First, to address the challenge of fine-grained emotion classification, we investigate a variety of lexical, syntactic, knowledge-based, context-based and class-specific features, and show how much these features contribute to the performance of the machine learning classifiers. We also propose a method that automatically extracts syntactic patterns to build a rule-based classifier to improve the accuracy of identifying minority emotions. Second, to deal with the challenge of manual annotation, we leverage emotion hashtags to harvest Twitter `big data' and collect millions of self-labeled emotion tweets, the labeling quality of which is further improved by filtering heuristics. We discover that the size of the training data plays an important role in emotion identification task as it provides a comprehensive coverage of different emotion-triggering events/situations. Further, the unigram and bigram features alone can achieve a performance that is competitive with the best performance of using a combination of ngram, knowledge-based and syntactic features. Third, to handle the paucity of the labeled emotion datasets in many domains, we seek to exploit the abundant self-labeled tweet collection to improve emotion identification in text from other domains, e.g., blog posts, fairy tales. We propose an effective data selection approach to iteratively select source data that are informative about the target domain, and use the selected data to enrich the target domain training data. Experimental results show that the proposed method outperforms the state-of-the-art domain adaptation techniques on datasets from four different domains including blog, experience, diary and fairy tales.
Views: 382 Knoesis Center
Week 8: Basic Text Feature Extraction
Carolyn Rose discusses basic text feature extraction for week 8 of DALMOOC.
Words as Features for Learning - Natural Language Processing With Python and NLTK p.12
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: 67449 sentdex
Python Tutorial: CSV Module - How to Read, Parse, and Write CSV Files
In this Python Programming Tutorial, we will be learning how to work with csv files using the csv module. We will learn how to read, parse, and write to csv files. CSV stands for "Comma-Separated Values". It is a common format for storing information. Knowing how to read, parse, and write this information to files will open the door to working with a lot of data throughout the world. Let's get started. The code from this video can be found at: https://github.com/CoreyMSchafer/code_snippets/tree/master/Python-CSV If you enjoy these videos and would like to support my channel, I would greatly appreciate any assistance through my Patreon account: https://www.patreon.com/coreyms Or a one-time contribution through PayPal: https://goo.gl/649HFY If you would like to see additional ways in which you can support the channel, you can check out my support page: http://coreyms.com/support/ Equipment I use and books I recommend: https://www.amazon.com/shop/coreyschafer You can find me on: My website - http://coreyms.com/ Facebook - https://www.facebook.com/CoreyMSchafer Twitter - https://twitter.com/CoreyMSchafer Google Plus - https://plus.google.com/+CoreySchafer44/posts Instagram - https://www.instagram.com/coreymschafer/ #Python
Views: 228004 Corey Schafer
Scikit-Learn incorporation - Natural Language Processing With Python and NLTK p.15
Despite coming packed with some classifiers, NLTK is mainly a toolkit focused on natural language processing, and not machine learning specifically. A module that is focused on machine learning is scikit-learn, which is packed with a large array of machine learning algorithms which are optimized in C. Luckily NLTK has recognized this and comes packaged with a special classifier that wraps around scikit learn. In NLTK, this is: nltk.classify.scikitlearn, specifically the class: SklearnClassifier is what we're interested in. This allows us to port over any of the scikit-learn classifiers that are compatible, which is most. 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: 43011 sentdex
Machine Learning with Text  - Count Vectorizer Sklearn (Spam Filtering example Part 1 )
#MachineLearningText #NLP #CountVectorizer #DataScience #ScikitLearn #TextFeatures #DataAnalytics #MachineLearning Text cannot be used as an input to ML algorithms, therefore we use certain techniques to extract features from text. Count Vectorizer extracts features based on word count. 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/
Views: 26894 The Semicolon
Detecting Dictionary DGAs using Machine Learning Video
Hackers are getting smarter every day. They are figuring out how to outsmart vendors providing cybersecurity solutions. Here at Infoblox, our data scientists are heads down finding remedies for algorithmically generated domains (called domain generation algorithms). In this community webinar, Infoblox Data Scientist, Mayana Pereira discusses one of the Infoblox projects - using machine learning to detect dictionary DGAs (the latest kind of DGAs that can escape most cybersecurity vendors' tools these days). This is a webinar that is a must watch!
Views: 151 Infoblox Community
Lecture 6: Dependency Parsing
Lecture 6 covers dependency parsing which is the task of analyzing the syntactic dependency structure of a given input sentence S. The output of a dependency parser is a dependency tree where the words of the input sentence are connected by typed dependency relations. Key phrases: Dependency Parsing. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Sentiment analysis 2: Load Data
A Machine Learning and Natural Language Processing application: Build a model to predict whether a movie review is positive or negative. Download the movie review data set: Large Movie Review Dataset v1.0 Collected by Andrew Maas from Stanford. http://ai.stanford.edu/~amaas/data/sentiment/index.html Links to the Jupyter notebook used in the tutorial can be found in description of the last video of the series. My LinkedIn: https://www.linkedin.com/in/weihua-zheng-compbio/
Views: 510 William.Zheng
Python TF-IDF (NLP) Calculation Introduction
In this tutorial I will introduce what we will be building in the following videos. Source code can be found at my GitHub: https://github.com/Simon-Fukada My linkedIn: www.linkedin.com/in/simon-fukada
Views: 154 ProgrammingTogether
BigML Fall 2016 Webinar - Topic Models!
Our Fall 2016 release brings Topic Models, the latest resource that helps you easily find thematically related terms in your text data. Discover BigML’s implementation of the underlying Latent Dirichlet Allocation (LDA) technique, one of the most popular probabilistic methods for topic modeling tasks. This resource is included in our FREE version and it is accessible from the BigML Dashboard as well as the API. Topic Models not only help you better understand and organize your collection of documents, but also can improve the performance of your models for information retrieval tasks, collaborative filtering, or when assessing document similarity. More info: https://bigml.com/releases/fall-2016
Views: 1175 bigmlcom
Obiamaka Agbaneje - Building a Naive Bayes Text Classifier with scikit learn
Building a Naive Bayes Text Classifier with scikit-learn [EuroPython 2018 - Talk - 2018-07-26 - PyCharm [PyData]] [Edinburgh, UK] By Obiamaka Agbaneje Machine learning algorithms used in the classification of text are Support Vector Machines, k Nearest Neighbors but the most popular algorithm to implement is Naive Bayes because of its simplicity based on Bayes Theorem. The Naive Bayes classifier is able to memorise the relationships between the training attributes and the outcome and predicts by multiplying the conditional probabilities of the attributes with the assumption that they are independent of the outcome. It is popularly used in classifying data sets that have a large number of features that are sparse or nearly independent such as text documents. In this talk, I will describe how to build a model using the Naive Bayes algorithm with the scikit-learn library using the spam/ham youtube comment dataset from the UCI repository. Preprocessing techniques such as Text normalisation and Feature extraction will be also be discussed. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2018.europython.eu/en/speaker-release-agreement/
The Zen of Python, decoded.
The "Zen of Python" is an amazing poem written by Tim Peters. It is a collection of 20 software principles which guide the design of Python Programming Language. The Zen of Python, by Tim Peters Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those! More awesome topics covered here: WhatsApp Bot using Twilio and Python: http://bit.ly/2JmZaNG Discovering Hidden APIs: http://bit.ly/2umeMHb RegEx in Python: http://bit.ly/2Hhtd6L Introduction to Numpy: http://bit.ly/2RZMxvO Introduction to Matplotlib: http://bit.ly/2UzwfqH Introduction to Pandas: http://bit.ly/2GkDvma Intermediate Python: http://bit.ly/2sdlEFs Functional Programming in Python: http://bit.ly/2FaEFB7 Python Package Publishing: http://bit.ly/2SCLkaj Multithreading in Python: http://bit.ly/2RzB1GD Multiprocessing in Python: http://bit.ly/2Fc9Xrp Parallel Programming in Python: http://bit.ly/2C4U81k Concurrent Programming in Python: http://bit.ly/2BYiREw Dataclasses in Python: http://bit.ly/2SDYQub Exploring YouTube Data API: http://bit.ly/2AvToSW Jupyter Notebook (Tips, Tricks and Hacks): http://bit.ly/2At7x3h Decorators in Python: http://bit.ly/2sdloX0 Inside Python: http://bit.ly/2Qr9gLG Exploring datetime: http://bit.ly/2VyGZGN Computer Vision for noobs: http://bit.ly/2RadooB Python for web: http://bit.ly/2SEZFmo Awesome Linux Terminal: http://bit.ly/2VwdTYH Tips, tricks, hacks and APIs: http://bit.ly/2Rajllx Optical Character Recognition: http://bit.ly/2LZ8IfL Facebook Messenger Bot Tutorial: http://bit.ly/2BYjON6 #python #import #this
Views: 1837 Indian Pythonista
Neural Question Answering over Knowledge Graphs
Questions in real-world scenarios are mostly factoid, such as "any universities in Seattle?''. In order to answer factoid questions, a system needs to extract world knowledge and reason over facts. Knowledge graphs (KGs), e.g., Freebase, NELL, YAGO etc, provide large-scale structured knowledge for factoid question answering. What we do is usually parsing the raw questions into path queries of KGs. This talk introduces three pieces of work in different abstraction levels to handle this challenge: i) In case a path query, containing the topical entity and relation chain referred by a question, is available precisely in a KG, how to perform effective path query answering over KGs directly -- KGs usually suffer from severe sparsity. The first part of this talk presents three sequence-to-sequence models for path query answering and vector space learning of KG elements (entities & relations); ii) As questions in reality are raw text and mostly contain single-relation, the second part of this talk presents an effective entity linker and an attentive max-pooling based convolutional neural network to conduct (question, single KG fact) match, which enables the system to pick the best KG fact -- a one-hop path query -- to retrieve the answer; iii) Subsequently, the final part shows how to make improvements over single-relation KGQA to handle the multi-relation KGQA problem -- projecting the multi-relation question into a multi-hop path query for answer retrieval.  See more on this video at https://www.microsoft.com/en-us/research/video/neural-question-answering-knowledge-graphs/
Views: 3168 Microsoft Research
Natural Language Processing APIs: Elena Álvarez Mellado (Apicultur) at APIdays Mediterranea 2015
Elena Álvarez Mellado (from Apicultur) speaks at APIdays Mediterranea 2015: Introducing Natural Language Processing. APIdays Mediterranea in the main independent conference in Europe that gathers together the community around APIs, data and Natural Language Processing in Barcelona. Check next editions at: http://mediterranea.apidays.io/
Views: 490 Apicultur
Scikit Learn Feature Extraction
We talk about feature extraction and some of the basic tools needed to do NLP including bag of words and vectorizers. Associated Github Commit: https://github.com/knathanieltucker/bit-of-data-science-and-scikit-learn/blob/master/notebooks/FeatureExtraction.ipynb Associated Scikit Links: http://scikit-learn.org/stable/modules/feature_extraction.html
Views: 3632 Data Talks
data.bythebay.io: Niyati Parameswaran, SAMEntics : Tools for paraphrase detection
Sparse Ground Truth, mediocre quality of training data, limited representation of novel queries, heavy biases due to human intervention and large time overheads associated with manual cluster creation are inconveniences that both partners and the Watson Ecosystem technical team face on a day-to-day basis. Enriching Ground Truth, boosting the quality of training data, factoring in for novel queries and minimizing biases & time sucks due to human intervention therefore emerge as preprocessing requirements that are crucial to meeting the needs of a more seamless transition into when utilizing a cognitive service that is powered by Watson. SAMEntics(Same + Semantics) has been conceptualized to match this exact purpose and provides an efficient alternative to handling large volumes of text across domains to scale. It comprises tools for paraphrase detection and paraphrase generation and is directed at 1. discovering rewording in sentences across domains 2. bucketing hierarchical categories within domains by capturing intent 3. expediting question(s)-answer(s) mapping 4. rendering syntactically correct phrasal variations of sentences while retaining semantic meaning to enrich partner ground truth, boost training data quality and minimize biases and time sucks due to human intervention. SAMEntics thus provides an intelligent alternative to handling large volumes of text efficiently by not only automatically rendering clusters based off user intent in a hierarchical manner but also by generating rewordings of user queries in the case of sparse and(or) poor quality training data. Join us as we go over the current and emerging state-of-the-art in this space. Reflect on what is changing the world in this era of cognition. Dive deep into the pipeline and the core algorithmic paradigms that power a paraphrase detection and paraphrase generation engine. And leave with an understanding of what it takes to build a product that provides data science-as-a-service. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 585 FunctionalTV
PyData - Recent advancements in NLP and Deep Learning  A Quant s Perspective by Umit Mert Cakmak
There is a gold-rush among hedge-funds for text mining algorithms to quantify textual data and generate trading signals. Harnessing the power of alternative data sources became crucial to find novel ways of enhancing trading strategies. With the proliferation of new data sources, natural language data became one of the most important data sources which could represent the public sentiment and opinion about market events, which then can be used to predict financial markets. Talk is split into 5 parts; 1. Who is a quant and how do they use NLP? 2. How deep learning has changed NLP? 3. Let’s get dirty with word embeddings 4. Performant deep learning layer for NLP: The Recurrent Layer 5. Simple Algorithmic Trading System 1. Who is a quant and how do they use NLP? Quants use mathematical and statistical methods to create algorithmic trading strategies. Due to recent advances in available deep learning frameworks and datasets (time series, text, video etc) together with decreasing cost of parallelisable hardware, quants are experimenting with various NLP methods which are applicable to quantitative trading. In this section, we will get familiar with the brief history of text mining work that quants have done so far and recent advancements. 2. How deep learning has changed NLP? In recent years, data representation and modeling methods are vastly improved. For example when it comes to textual data, rather than using high dimensional sparse matrices and suffering from curse of dimensionality, distributional vectors are more efficient to work with. In this section, I will talk about distributional vectors a.k.a. word embeddings and recent neural network architectures used when building NLP models. 3. Let’s get dirty with word embeddings Models such as Word2vec or GloVe helps us create word embeddings from large unlabeled corpus which represent the relation between words, their contextual relationships in numerical vector spaces and these representations not only work for words but also could be used for phrases and sentences. In this section, I will talk about inner workings of these models and important points when creating domain-specific embeddings (e.g. for sentiment analysis in financial domain). 4. Performant deep learning layer for NLP: The Recurrent Layer Recurrent Neural Networks (RNNs) can capture and hold the information which was seen before (context), which is important for dealing with unbounded context in NLP tasks. Long Short Term Memory (LSTM) networks, which is a special type of RNN, can understand the context even if words have long term dependencies, words which are far back in their sequence. In this talk, I will compare LSTMs with other deep learning architectures and will look at LSTM unit from a technical point of view. 5. Simple Algorithmic Trading System Financial news, especially if it’s major, can change the sentiment among investors and affect the related asset price with immediate price corrections. For example, what’s been communicated in quarterly earnings calls might indicate whether the price of share will drop or increase based on the language used. If the message of the company is not direct and featuring complex sounding language, it usually indicates that there’s some shady stuff going on and if this information extracted right, it’s a valuable trading signal. For similar reasons, scanning announcements and financial disclosures for trading signals became a common NLP practice in investment industry. In this section, I will talk about the various data sources that researchers can use and also explain common NLP workflows and deep learning practices for quantifying textual data for generating trading signals. I will end with summary with application architecture in case anyone would like to implement similar systems for their own use. Machine Learning Deep Learning Natural Language Processing Algorithmic Trading Watson Machine Learning Watson Natural Language Understanding
Views: 124 techallday
Python+Machine Learning tutorial - Working with textual data
Machine Learning with text data can be very useful for social networks analytics for instance to perform sentiment analysis. Extracting a "machine learnable" representation from raw text is an art in itself. In this session we will introduce the bag of words representation and its implementation in scikit-learn via its text vectorizers. We will discuss preprocessing with NLTK, n-grams extractions, TF-IDF weighting and the use of SciPy sparse matrices. Finally we will use that data to train and evaluate a Naive Bayes classifier and a Linear Support Vector Machine.
Views: 920 Microsoft Research
Deep Learning with Python, TensorFlow, and Keras tutorial
An updated deep learning introduction using Python, TensorFlow, and Keras. Text-tutorial and notes: https://pythonprogramming.net/introduction-deep-learning-python-tensorflow-keras/ TensorFlow Docs: https://www.tensorflow.org/api_docs/python/ Keras Docs: https://keras.io/layers/about-keras-layers/ Discord: https://discord.gg/sentdex
Views: 238160 sentdex
This system is developed as a part of academic main project. This system is fully written in python and you can use it to predict the authorship of an unknown document. You can get the source code at https://github.com/nithinp/authorship-predictor
Views: 688 nithin p
Skip Gram
Views: 257 puertosaber
Apache Hadoop Tutorial | Hadoop Tutorial For Beginners | Big Data Hadoop | Hadoop Training | Edureka
** Flat 20% Off (Use Code: YOUTUBE20) Hadoop Training: https://www.edureka.co/hadoop ** This Edureka "Hadoop tutorial For Beginners" ( Hadoop Blog series: https://goo.gl/LFesy8 ) will help you to understand the problem with traditional system while processing Big Data and how Hadoop solves it. This tutorial will provide you a comprehensive idea about HDFS and YARN along with their architecture that has been explained in a very simple manner using examples and practical demonstration. At the end, you will get to know how to analyze Olympic data set using Hadoop and gain useful insights. Below are the topics covered in this tutorial: 1. Big Data Growth Drivers 2. What is Big Data? 3. Hadoop Introduction 4. Hadoop Master/Slave Architecture 5. Hadoop Core Components 6. HDFS Data Blocks 7. HDFS Read/Write Mechanism 8. What is MapReduce 9. MapReduce Program 10. MapReduce Job Workflow 11. Hadoop Ecosystem 12. Hadoop Use Case: Analyzing Olympic Dataset Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Hadoop playlist here: https://goo.gl/ExJdZs Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #edurekaHadoop #HadoopTutorial #Hadoop #HadoopTutorialForBeginners #HadoopArchitecture #LearnHadoop #HadoopTraining #HadoopCertification How it Works? 1. This is a 5 Week Instructor led Online Course, 40 hours of assignment and 30 hours of project work 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 Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert Hadoop instructors will help you: 1. Master the concepts of HDFS and MapReduce framework 2. Understand Hadoop 2.x Architecture 3. Setup Hadoop Cluster and write Complex MapReduce programs 4. Learn data loading techniques using Sqoop and Flume 5. Perform data analytics using Pig, Hive and YARN 6. Implement HBase and MapReduce integration 7. Implement Advanced Usage and Indexing 8. Schedule jobs using Oozie 9. Implement best practices for Hadoop development 10. Work on a real life Project on Big Data Analytics 11. Understand Spark and its Ecosystem 12. Learn how to work in RDD in Spark - - - - - - - - - - - - - - Who should go for this course? If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career: 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building successful career in Big Data - - - - - - - - - - - - - - Why Learn Hadoop? Big Data! A Worldwide Problem? According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data. - - - - - - - - - - - - - - Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka's Hadoop Online course and carve a niche for yourself! For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Customer Review: Michael Harkins, System Architect, Hortonworks says: “The courses are top rate. The best part is live instruction, with playback. But my favorite feature is viewing a previous class. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! ~ This is the killer education app... I've take two courses, and I'm taking two more.”
Views: 342683 edureka!
Concept Extraction Final Project
Concept Extraction Final Project Demonstration. Software and Information Systems Engineering, BGU
Views: 38 Eitan Shteinberg
Data Science & Machine Learning - Naive Bayes Handson- DIY- 32 -of-50
Data Science & Machine Learning - Naive Bayes Handson- DIY- 32 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU Naïve Bayes – Probabilistic Classification Get the data from UCI YouTube+Spam+Collection Dataset Citation Request: We would appreciate:  1. If you find this collection useful, make a reference to the paper below and the web page: [Web Link].  2. Send us a message either to talmeida AT ufscar.br or tuliocasagrande AT acm.org in case you make use of the corpus.  http://dcomp.sor.ufscar.br/talmeida/youtubespamcollection/ Load and clean up the data, divide the text into individual words using tm_map(). #lower case #remove stopwords / filler words such as to, and, but etc. #remove punctuations #remove numbers #strip white spaces; #Stemming; Create a DTM sparse matrix – a table with the frequency of words in each line. Naïve Bayes Model – without & with Laplace Estimator Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50 Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Intro - DIY- 25 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Use Case - DIY- 26 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Exercise - DIY- 27 -of-50 Data Science & Machine Learning - Random Forest Intro - DIY- 28 -of-50 Data Science & Machine Learning - Random Forest Hands on - DIY- 29 -of-50 Data Science & Machine Learning - Naive Bayes - DIY- 31 -of-50 Data Science & Machine Learning - Naive Bayes Handson- DIY- 32 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree, Random Forest, Naive Bayes
The Long Road from Text to Meaning
Google Tech Talks May 3, 2007 ABSTRACT Computers have given us a new way of thinking about language. Given a large sample of language, or corpus, and computational tools to process it, we can approach language as physicists approach forces and chemists approach chemicals. This approach is noteworthy for missing out what, from a language-user's point of view, is important about a piece of language: its meaning. I shall present this empiricist approach to the study of language and show how, as we develop accurate tools for lemmatisation, part-of-speech tagging and parsing, we move from the raw input -- a character stream -- to an analysis of that stream in increasingly rich terms: words, lemmas,...
Views: 11260 Google
#7PyData - Wojciech Walczak - Semantic similarity measuring using recursive auto-encoders..
Bartosz Biskupski & Wojciech Walczak (Samsung) - How much meaning can you pack into a real-valued vector? Semantic similarity measuring using recursive auto-encoders. http://underfitted.org/slides/WojciechWalczak_ParaphraseDetection_PyDataWarsaw.pdf The presentation will start with a brief overview of AI research and development at Samsung R&D in Poland. We will then describe a solution, developed in one of our projects, that has won the Semantic Textual Similarity (STS) task within the SemEval 2016 research competition. The goal of this competition was to measure semantic similarity between two given sentences on a scale from 0 to 5. At the same time the solution should replicate human language understanding. The presented model is a novel hybrid of recursive auto-encoders (a deep learning technique) and a WordNet award-penalty system, enriched with a number of other similarity models and features used as input for Linear Support Vector Regression.
Views: 407 PyData Warsaw
Intro and loading Images  - OpenCV with Python for Image and Video Analysis 1
Welcome to a tutorial series, covering OpenCV, which is an image and video processing library with bindings in C++, C, Python, and Java. OpenCV is used for all sorts of image and video analysis, like facial recognition and detection, license plate reading, photo editing, advanced robotic vision, optical character recognition, and a whole lot more. We will be working through many Python examples here. Getting started with OpenCV's Python bindings is actually much easier than many people make it out to be initially. You will need two main libraries, with an optional third: python-OpenCV, Numpy, and Matplotlib. Sample code and text-based tutorial: https://pythonprogramming.net/loading-images-python-opencv-tutorial/ http://pythonprogramming.net https://twitter.com/sentdex
Views: 504713 sentdex
UTTR.com Chatbots Conference on June 1, 2017 in Los Angeles: AI, NLP Bot Systems & Investment
The UTTR Chatbots Conference will be on June 1, 2017 in Los Angeles. The conference chatbots, artificial intelligence, NLP (natural language processes), bot systems, incident response, messaging platforms, customer service, mobile and desktop apps. Venture capital and investors will also be present. For more information, please visit http://UTTR.com
Views: 28 UT TR
Intro to machine learning on Google Cloud Platform (Google I/O '18)
There are revolutionary changes happening in hardware and software that are democratizing machine learning (ML). Whether you're new to ML or already an expert, Google Cloud Platform has a variety of tools for users. This session will start with the basics: using a pre-trained ML model with a single API call. It'll then look at building and training custom models with TensorFlow and Cloud ML Engine, and will end with a demo of AutoML Vision - a new tool for training a custom image classification model without writing model code. Rate this session by signing-in on the I/O website here → https://goo.gl/4n5aYA Watch more GCP sessions from I/O '18 here → https://goo.gl/qw2mR1 See all the sessions from Google I/O '18 here → https://goo.gl/q1Tr8x Subscribe to the Google Cloud Platform channel → https://goo.gl/S0AS51 #io18
Views: 36825 Google Cloud Platform
Implemention of Feature Extraction Algorithm on FPGA - Fall 2014 (SJSU)
Implemention of Feature Extraction Algorithm on FPGA in which image is read from SDcard and displayed on VGA monitor.
Views: 133 Akshat Agrawal
Pontus Stenetorp: Natural Language Processing with Julia
Pontus Stenetorp: Natural Language Processing with Julia Manchester Julia Workshop http://www.maths.manchester.ac.uk/~siam/julia16/
Views: 507 SIAM Manchester
Homework7: Unigram/Bigram Classifier Solution
https://sellfy.com/p/8pKq/ Build a Spam Classifier using Machine Learning and ElasticSearch. Data Consider the trec07_spam set of documents annotated for spam, available “data resources”. First read and accept agreement at http://plg.uwaterloo.ca/~gvcormac/treccorpus07/. Then download the 255 MB Corpus (trec07p.tgz). The html data is in data/; the labels ("spam" or "ham") are in full/. Index the documents with ElasticSearch, but use library to clean the html into plain test first. You dont have to do stemming or skipping stopwords (up to you); eliminating some punctuation might be useful. Cleaning Data is Required: By "unigram" we mean an English word, so as part of reading/processing data there will be a filter step to remove anything that doesnt look like an English word or small number. Some mistake unigrams passing the filter are acceptable, if they look like words (e.x. "artist_", "newyork", "grande") as long as they are not overwhelming the set of valid unigrams. You can use any library/script/package for cleaning, or share your cleaning code (but only the cleaning code) with the other students. Make sure to have a field “label” with values “yes” or “no” (or "spam"/"ham") for each document. Partition the spam data set into TRAIN 80% and TEST 20%. One easy way to do so is to add to each document in ES a field "split" with values either "train" or "test" randomly, following the 80%-20% rule. Thus there will be 2 feature matrices, one for training and one for testing (different documents, same exact columns/features). The spam/ham distribution is roughly a third ham and two thirds spam; you should have a similar distribution in both TRAIN and TEST sets. Part1: Manual Spam Features Manually create a list of ngrams (unigrams, bigrams, trigrams, etc) that you think are related to spam. For example : “free” , “win”, “porn”, “click here”, etc. These will be the features (columns) of the data matrix. Next iteration of the course: dont use your word/features, instead use the ones from this list. You will have to use ElasticSearch querying functionality in order to create feature values for each document, for each feature. There are ways to ask ES to give all matches (aka feature values) for a given ngram, so you dont have to query (ngram, doc) for all docs separately. For part 1, you can use a full matrix since the size wont be that big (docs x features). However, for part 2 you will have to use a sparse matrix, since there will be a lot more features. Train a learning algorithm The label, or outcome, or target are the spam annotation “yes” / “no” or you can replace that with 1/0. Using the “train” queries static matrix, train a learner to compute a model relating labels to the features on TRAIN set. You can use a learning library like SciPy/NumPy, C4.5, Weka, LibLinear, SVM Light, etc. The easiest models are linear regression and decision trees. Test the spam model Test the model on TEST set. You will have to create a testing data matrix with feature values in the same exact way as you created the training matrix: use ElasticSearch to query for your features, use the scores are feature values. Run the model to obtain scores Treat the scores as coming from an IR function, and rank the documents Display first few “spam” documents and visually inspect them. You should have these ready for demo. IMPORTANT : Since they are likely to be spam, if you display these in a browser, you should turn off javascript execution to protect your computer. Part 2: All unigrams as features A feature matrix should contain a column/feature for every unigram extracted from training documents. You will have to use a particular data format described in class (note, toy example), since the full matrix becomes too big. Write the matrix and auxiliary files on disk. Given the requirements on data cleaning, you should not have too many unigrams, but still many enough to have to use a sparse representation. Extracting all unigrams using Elastic Search calls This is no diffeernt than part1 in terms of the ES calls, but you'd have to first generate a list with all unigrams. Training and testing Once the feature matrices are ready (one for training, the second for testing), run either LibLinear Regression (with sparse input) or a learning algorithm implemented by us to take advantage of the sparse data representations. Feature analysis Identify from the training log/model the top (most important) spam unigrams. Do they match your manual spam features from part 1?
Views: 123 Best Sln
Sentiment and Social Network Analysis — Laura Drummer,  Novetta Solutions
Traditional social network analysis is performed on a series of nodes and edges, generally gleaned from metadata about interactions between several actors. In the intelligence and law enforcement communities, this metadata can frequently be paired with data and communications content. Our analytic, SocialBee, takes advantage of this widely untapped data source to not only perform more in-depth social network analysis based on actor behavior, but also enrich the social network analysis with topic modelling, sentiment analysis, and trending over time. Through extraction and analysis of topic-enriched links, SocialBee has also been able to successfully predict hidden relationships, i.e., relationships not seen in the original dataset, but that exist in an external dataset via different means of communication. The clustering of communities based on behavior over time can be done by looking purely at metadata, but SocialBee also analyzes the content of communications which will allows for a richer analysis of the tone, topic, and sentiment of each interaction. Traditional topic modelling is usually done using natural language processing to build clusters of similar words and phrases. By incorporating these topics into a communications network stored in neo4j, we are able to ask much more meaningful questions about the nature of individuals, relationships, and entire communities. Using its topic modelling features, SocialBee can identify behavior based communities within this networks. These communities are based on relationships where a significant percentage of the communications are about a specific topic. In these smaller networks, it is much easier to identify influential nodes for a specific topic, and find disconnected nodes in a community. This talk explores the schema designed to store this data in neo4j, which is loosely based on the concept of the 'Author-Recipient-Topic' model as well as several advanced queries exploring the nature of relationships, characterizing sub-graphs, and exploring the words that make up the topics themselves. Speaker: Laura Drummer Location: GraphConnect NYC 2017
Views: 502 Neo4j