Text Feature Extraction Python Github

Learn the benefits and applications of local feature detection and extraction. Products News API Search, source, and analyze news from around the web in real-time Text Analysis API Extract meaning and insight from textual content with ease Text Analysis Platform Build a model tailored to your solution, then deploy and maintain it with ease Pricing News API Pricing Choose the News API package that matches your requirements. Let’s do the code. Filtering an image by Gabor wavelet is one of the widely used methods for feature extraction. scikit-learn is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages (numpy, scipy, matplotlib). walk through the code which is available on Github. Download the file for your platform. And during prediction time, HOG feature is extracted from the real image and then the prediction is made. We convert text to a numerical representation called a feature vector. 29-Apr-2018 – Added Gist for the entire code; NER, short for Named Entity Recognition is probably the first step towards information extraction from unstructured text. 2) Tokenize the text. 1: Python Machine learning projects on GitHub, with color corresponding to commits/contributors. This blog is a gentle introduction to text summarization and can serve as a practical summary of the current landscape. from mlxtend. model_selection. feature_extraction. Text on GitHub with a CC-BY-NC-ND license Code on GitHub with a MIT license Go to Chapter 8 : Machine Learning Get the Jupyter notebook. text import. The full code used to extract the features and run the t-SNE is. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. The following are code examples for showing how to use sklearn. As the problem of information overload has grown, and as the quantity of data has increased, so has interest in automatic summarization. This section addresses basic image manipulation and processing using the core scientific modules NumPy and SciPy. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. TransactionEncoder. CountVectorizer(). If you want to use the text of the html page as a feature (e. PDF | Selection of text feature item is a basic and important matter for text mining and information retrieval. We would love to hear your feedback on this classification sample - you can send us your feedback and comments via the GitHub issues page. The dataset used in this example is the 20 newsgroups dataset which will be automatically downloaded and then cached and reused for the document classification example. Tf means term-frequency while tf–idf means term-frequency times inverse document-frequency. We built two word embedding models (using gensim), one using text from the titles and the other using prose texts. Finally, each corpus is transformed into vector space model (VSM) using the tf-idf vectorizer in Python's SKlearn package to extract the features. There is no concept of input and output features in time series. Also try practice problems to test & improve your skill level. You can vote up the examples you like or vote down the ones you don't like. feature_extraction. 2) Tokenize the text. TfidfVectorizer taken from open source projects. This is an excerpt from the Python Data Science Handbook by Jake VanderPlas; Jupyter notebooks are available on GitHub. The feature we'll use is TF-IDF, a numerical statistic. Shubham Jain, February 27, (with Python and R Codes) Add Shine to your Data Science Resume with these 8 Ambitious Projects on GitHub. io/ TextBlob is a Python (2 and 3) library for processing textual data. Built-in deep learning models. Whether the feature should be made of word or character n-grams. They are extracted from open source Python projects. This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. Applying Entity Extraction To The Russian Twitter Troll Dataset. Feature Extraction & Training. CNN feature extraction in TensorFlow is now made easier using the tensorflow/models repository on Github. Apart from just getting text from the page (e. Melusine is a high-level. Machine Learning :: Text feature extraction (tf-idf) - Part II; The effective receptive field on CNNs; A sane introduction to maximum likelihood estimation (MLE) and maximum a posteriori (MAP) Deep learning - Convolutional neural networks and feature extraction with Python; Simple and effective coin segmentation using Python and OpenCV. because it has too low statistics) and select only the relevant features next:. feature_extraction. A digital image in its simplest form is just a matrix of pixel intensity values. At the application level, a library for feature extraction and classification in Python will be developed. The post is organized as follows: by a feature extraction step performed. The following are code examples for showing how to use sklearn. Clone via HTTPS Clone with Git or checkout with SVN using the repository’s web address. Applying Entity Extraction To The Russian Twitter Troll Dataset. We can use it to extract and count words from a document, build a vocabulary and more. Pliers is a Python package for automated extraction of features from multimodal stimuli. We convert text to a numerical representation called a feature vector. NLTK doesn't include a paragraph tokenizer, so we'll try to create our own. Text themselves cannot be used by machine learning models. One logic that may work is this: a paragraph is detected if there are consecutive newline characters. jgTextRank : Yet another Python implementation of TextRank This is a parallelisable and highly customisable implementation of the TextRank algorithm [Mihalcea et al. Interactive Course Feature Engineering for NLP in Python. A clear candidate for feature reduction is text learning, since the data has such high dimension. iFeature: A Python package and web server for features extraction and selection from protein and peptide sequences Article (PDF Available) in Bioinformatics 34(14) · March 2018 with 559 Reads. Local Feature Detection and Extraction. You’ll also need the Python library called bokeh, used to create the interactive html plots. text import. This is an excerpt from the Python Data Science Handbook by Jake VanderPlas; Jupyter notebooks are available on GitHub. Automatic keyword extraction: using cosine similarities as features from sklearn. text import # Create the bag of words feature Everything on this site is available on GitHub. Also try practice problems to test & improve your skill level. If you want to use "bag-of-words" feature, in other words, not to care the relative position of features, You don't need to put such identifiers. TfidfTransformer(). , you select certain features, train a classifier, evaluate it's performance, and if it is not s. class sklearn. We analyze Top 20 Python Machine learning projects on GitHub and find that scikit-Learn, PyLearn2 and NuPic are the most actively contributed projects. TfidfVectorizer(). Document Clustering with Python In this guide, I will explain how to cluster a set of documents using Python. For Python users, there is an easy-to-use keyword extraction library called RAKE, which stands for Rapid Automatic Keyword Extraction. Why extract keywords? Extracting keywords is one of the most important tasks while working with text data in the domain of Text Mining, Information Retrieval and Natural Language Processing. FIXME explain L2. Feature Selection for Machine Learning. If you’re interested in its workings, it is very well explained in this blog. Your feedback is welcome, and you can submit your comments on the draft GitHub issue. Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. svm import LinearSVC from sklearn. Thanks for this @yanofsky - its awesome code. FIXME explain L2. DLPy provides a convenient way to apply deep learning functionalities to solve computer vision, NLP, forecasting and speech processing problems. Time series feature engineering is a time-consuming process because scientists and engineers have to consider the multifarious algorithms of signal processing and time series analysis for identifying and extracting meaningful features from time series. feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image. py install you can install the latest development version from the GitHub repository by executing. walk through the code which is available on Github. Installation. See why word embeddings are useful and how you can use pretrained word embeddings. >>> text = """Automatic summarization is the process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. I’ve included running times for both solutions, so we could have precise information about the cost that each one takes, in addition to their results. As we have seen throughout our CBIR module, keypoints and local invariant descriptors are critical for building high accuracy and scalable image search engines. 07/03/2019; 2 minutes to read +6; In this article. It takes lots of memory and more time for matching. Sample pipeline for text feature extraction and evaluation¶. Text feature extraction is the process of transforming what is essentially a list of words into a feature set that is usable by a classifier. feature_extraction. The following are code examples for showing how to use sklearn. feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image. With some basic features in hand, we explore techniques for classification and clustering on text, concluding the first few chapters of the book. The post is organized as follows: by a feature extraction step performed. More than 40 million people use GitHub to discover, fork, and contribute to over 100 million projects. Hosseini-Asl, R. This documentation is for scikit-learn version 0. They have feature selection metric inside the algorithm itself) The feature selection is also useful when you. Whether the feature should be made of word or character n-grams. from mlxtend. Additionally, we might want to extract new features by applying complex transformations on our original dataset. 07/03/2019; 2 minutes to read +6; In this article. Time Series data must be re-framed as a supervised learning dataset before we can start using machine learning algorithms. A function that performs one-hot encoding for class labels. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. The features created refer to : either a single column. The following are code examples for showing how to use sklearn. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. Perone Read the first part of this tutorial: Text feature extraction (tf-idf) – Part I. We have good feedback for every part of our project plan except for feature extraction. In this tutorial you will learn how to use OpenCV to detect text in natural scene images using the EAST text detector. The ultimate goal of this data massaging remains the same : feature extraction. Normally, people extract the HOG features from the image and then train it using SVM. Now we can use it to build features. However, in most cases you will likely benefit from the feature extraction infrastructure that ClearTK provides to accomplish a. ImageJ contains a macro language with which it is easy to extract features and then dump them into an ARFF file. To install from pypi: pip install python_speech_features From this. PDF | Selection of text feature item is a basic and important matter for text mining and information retrieval. The algorithm itself is described in the Text Mining Applications and Theory book by Michael W. The sklearn. This statistic uses term frequency and inverse document frequency. Sample Pipeline for Text Feature Extraction and Evaluation in Scikit-learn Note: this page is part of the documentation for version 3 of Plotly. For Python users, there is an easy-to-use keyword extraction library called RAKE, which stands for Rapid Automatic Keyword Extraction. Note Feature extraction is very different from Feature selection: the former consists in transforming arbitrary data. Text on GitHub with a CC-BY-NC-ND license Code on GitHub with a MIT license Go to Chapter 8 : Machine Learning Get the Jupyter notebook. PDF | Selection of text feature item is a basic and important matter for text mining and information retrieval. Runs with Python 2. feature_extraction. Information Extraction with Stanford NLP Introduction Open information extraction (open IE) refers to the extraction of structured relation triples from plain text, such that the schema for these relations does not need to be specified in advance. The bag of words model ignores grammar and order of words. TfidfTransformer¶ class sklearn. How to plot feature importance in Python calculated by the XGBoost model. The post is organized as follows: by a feature extraction step performed. Pipeline from nimbusml. scikit-learn is a very popular package for machine learning. TfidfTransformer(norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)¶ Transform a count matrix to a normalized tf or tf–idf representation. You can just provide the tool with a list of images. We'll show how to process it, analyze it and extract visual clusters from it. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. What is Analytics Zoo? Analytics Zoo provides a unified analytics + AI platform that seamlessly unites Spark, TensorFlow, Keras, PyTorch and BigDL programs into an integrated pipeline; the entire pipeline can then transparently scale out to a large Hadoop/Spark cluster for distributed training or inference. With the OCR feature, you can detect printed text in an image and extract recognized characters into a machine-usable character stream. I have used the following wrapper for convenient feature extraction in TensorFlow. SAS Deep Learning Python (DLPy) DLPy is a high-level Python library for the SAS Deep Learning features available in SAS ® Viya ®. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. Note that we recommend using the Python interface for this task, as for example in the filter visualization example. September 17, 2017 | 14 Minute Read I just recently joined an AI hackathon where we took on the challenging task of trying to recognize fake news. TfidfTransformer(). ML modeling is an iterative process and it is extremely important to keep track of your steps, dependencies between the steps, dependencies between your code and data files and all code running arguments. Text files are actually series of words (ordered). Whether the feature should be made of word or character n-grams. This is the default for extract_features if you do not hand in a default_fc_parameters at all. Extract n-gram features with scikit-learn. feature_extraction. Contribute to 1049451037/Webpage_Text_Extraction development by creating an account on GitHub. Key features of OCR web API. You can do this using gensim. El-Baz, “Alzheimer’s Disease Diagnosis by Adaptation of 3D Convolutional Network”, Image Processing (ICIP), 2016 IEEE Int. Cell Types Cache¶ The CellTypesCache class provides a Python interface for downloading data in the Allen Cell Types Database into well known locations so that you don’t have to think about file names and directories. The Python package tsfresh (Time Series FeatuRe Extraction on basis of Scalable Hypothesis. NLTK doesn't include a paragraph tokenizer, so we'll try to create our own. Next Day Video 57,697. feature_extraction. all of this is done in just few lines of code and that is the beauty of python. You can vote up the examples you like or vote down the ones you don't like. Whether the feature should be made of word or character n-grams. This section lists 4 feature selection recipes for machine learning in Python. We discussed the extraction of such features from text in Feature Engineering ; here we will use the sparse word count features from the 20 Newsgroups corpus to show. Quickstart: Extract printed and handwritten text using the Computer Vision REST API and Python. It is the process of classifying text strings or documents into different categories, depending upon the contents of the strings. if i use pdfminer it converts whole pdf into text then it gives the result is their any possibilities to get the text of each page separately from pdf. For each article in our dataset, we'll compute TF-IDF values. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. png) ### Advanced Machine Learning with scikit-learn # Text Data Andreas C. In Python, you can use scikit learn library and you can import TF-IDF vectorizer. Write a high-level “controller script” using the MORF Python API. For each benchmark experiment, the explanation about how to install and use a particular registration method is given in the documentation. This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. Python or any other programming language to program with ABBYY Cloud OCR SDK. feature_extraction. I find that the best solution among those in the Stackoverflow page is python-docx. However, the task of extracting features can be quite time consuming. For Python users, there is an easy-to-use keyword extraction library called RAKE, which stands for Rapid Automatic Keyword Extraction. Any state-of-the-art image feature extraction algorithms (SIFT, SURF etc. Entity extraction is a subtask of information extraction, and is also known as Named-Entity Recognition (NER), entity chunking and entity identification. Python users come from all sorts of backgrounds, but computer science skills make the difference between a Python apprentice and a Python master. Shubham Jain, February 27, (with Python and R Codes) Add Shine to your Data Science Resume with these 8 Ambitious Projects on GitHub. Is there anything wrong with that ? I am getting an accuracy of zero. Document Clustering with Python In this guide, I will explain how to cluster a set of documents using Python. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input. The user can also extract features with Python or Matlab. There is no concept of input and output features in time series. With some basic features in hand, we explore techniques for classification and clustering on text, concluding the first few chapters of the book. feature_extraction. This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. In this recipe, we show how to handle text data with scikit-learn. preprocessing import one_hot. Below image shows 200 Gabor filters that can extract features from images almost as similar as a human visual system does. Text feature extraction is the process of transforming what is essentially a list of words into a feature set that is usable by a classifier. from mlxtend. CountVectorizer(). scikit-learn is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages (numpy, scipy, matplotlib). It provides the building blocks necessary to create music information retrieval systems. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Is there anything wrong with that ? I am getting an accuracy of zero. if i use pdfminer it converts whole pdf into text then it gives the result is their any possibilities to get the text of each page separately from pdf. Carolyn Rose discusses basic text feature extraction for week 8 of DALMOOC. Step 3: Extracting features from text files. This post contains recipes for feature selection methods. If you are not sure what MFCCs are, and would like to know more have a look at this MFCC tutorial. In order to run machine learning algorithms we need to convert the text files into numerical feature vectors. Use hyperparameter optimization to squeeze more performance out of your model. 1: Python Machine learning projects on GitHub, with color corresponding to commits/contributors. pke works only for Python 2. feature_extraction. The features which have the "minimal" attribute are used here. If we feed the MFCCs as features to a machine learning algorithm, these lower-order coefficients will make good features, since they represent some simple aspects of the spectral shape, while the higher-order coefficients that you discard are more noise-like and are not important to train on. We will be using bag of words model for our example. Parameters-----input : string {' filename ', ' file ', ' content '} If ' filename ', the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. Statistical and. See the release notes for details. Feature extraction from pure text. Fortunately, some researchers published urban sound dataset. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. To recap: my best single model was the "char_word_model",  which can. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. If building meaningful predictive models is something you care about, please get in touch. There are pre-trained VGG, ResNet, Inception and MobileNet models available here. This post is a continuation of the first part where we started to learn the theory and practice about text feature extraction and vector space model representation. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. feature_extraction. Text themselves cannot be used by machine learning models. Below image shows 200 Gabor filters that can extract features from images almost as similar as a human visual system does. But here we need more intensive data cleaning. Note Feature extraction is very different from Feature selection: the former consists in transforming arbitrary data. My motivating example is to identify the latent structures within the synopses of the top 100 films of all time (per an IMDB list). Currently I am doing: from sklearn. Here's how to get the pdf's: Go to https://www. Additionally, we might want to extract new features by applying complex transformations on our original dataset. $ pip install ipython # the console where we execute the code $ pip install requests # a library to pull the entire HTML page $ pip install BeautifulSoup # the real magic is here. We convert text to a numerical representation called a feature vector. CNN feature extraction in TensorFlow is now made easier using the tensorflow/models repository on Github. walk through the code which is available on Github. We would love to hear your feedback on this classification sample - you can send us your feedback and comments via the GitHub issues page. An example of relationship extraction using NLTK can be found here. Text Feature Extraction The pretrained language model of thai2fit can be used to convert Thai texts into vectors, after which said vectors can be used for various machine learning tasks such as classification, clustering, translation, question answering and so on. speech-recognition python feature-extraction Updated. Extract n-gram features with scikit-learn. Scikit-learn is a focal point for data science work with Python, so it pays to know which methods you need most. Each recipe was designed to be complete and standalone so that you can copy-and-paste it directly into you project and use it immediately. Text themselves cannot be used by machine learning models. To install from pypi: pip install python_speech_features From this. Clone via HTTPS Clone with Git or checkout with SVN using the repository’s web address. It basically means extracting what is a real world entity from the text (Person, Organization. – Akshat Joshi Feb 9 '18 at 13:55. You can vote up the examples you like or vote down the ones you don't like. It takes lots of memory and more time for matching. SIFT uses a feature descriptor with 128 floating point numbers. Break text down into its component parts for spelling correction, feature extraction, and phrase transformation; Learn how to do custom sentiment analysis and named entity recognition. png) ### Advanced Machine Learning with scikit-learn # Text Data Andreas C. Option ‘char_wb’ creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. text import CountVectorizer cvect_obj = CountVectorizer() vects = cvect_obj. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. Pliers is a Python package for automated extraction of features from multimodal stimuli. Sample pipeline for text feature extraction and evaluation¶. The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, language detection, and entity recognition. 07/03/2019; 2 minutes to read +6; In this article. I would like to add up PDFMiner and Slate to the queue PDFMiner PDFMiner is a tool for extracting information from PDF documents. I’m assum. High-throughput feature extraction. We converted the text to lowercase and removed punctuation. The motivation is the following. In this tutorial, we will extract features using a pre-trained model with the included C++ utility. The NLTK classifiers expect dict style feature sets, so we must therefore transform our text into a dict. We further show how these features could be used for unsupervised learning. There are two primary ways you can use textract. Statistical and. Python PDF parser and analyzer It has an extensible PDF parser that can be used for other purposes than text analysis. The face detector we use is made using the classic Histogram of Oriented Gradients (HOG) feature combined with a linear classifier, an image pyramid, and sliding window detection scheme. The rSAFE package is a model agnostic tool for making an interpretable white-box model more accurate using alternative black-box model called surrogate model. This section lists 4 feature selection recipes for machine learning in Python. load_songs. Installation. Dataset organization and enrichment used for the verification framework, data visualizations. Features is a simple implementation of feature set algebra in Python. Take an example of text classification problem where the training data contain category wise documents. Note that I did not include "linear SVC with L-1 based feature selection" model in the voting classifier, since it is the same model as Linear SVC, except for the fact that it filters out features first by L-1 regularization, and comparing the results linear SVC without the feature selection showed a better result. Here, we follow the existing Python implementation. text feature extraction. GitHub Gist: instantly share code, notes, and snippets. To install from pypi: pip install python_speech_features From this. The full code used to extract the features and run the t-SNE is. First, understand what is going on here. This class turns sequences of symbolic feature names (strings) into scipy. Note: We've recently added new features or fields to DOCUMENT_TEXT_DETECTION. Extract main text from HTML. - Akshat Joshi Feb 9 '18 at 13:55. In the advanced category, the tasks is to extract a list of attributes from each product listing given product title and the accompanied image (a text and a image input). The following table provides a brief overview of the most important methods used for data analysis. It is the process of classifying text strings or documents into different categories, depending upon the contents of the strings. E very classification problem in natural language processing (NLP) is broadly categorized as a document or a token level classification task. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. It provides a unified, standardized interface to dozens of different feature extraction tools and services--including many state-of-the-art deep learning-based APIs. Document Clustering with Python In this guide, I will explain how to cluster a set of documents using Python. if you have a long text and you want to extract keywords automatically. For Python users, there is an easy-to-use keyword extraction library called RAKE, which stands for Rapid Automatic Keyword Extraction. It contains 8,732 labelled sound clips (4 seconds each) from ten classes: air conditioner, car horn, children playing, dog bark, drilling, engine idling, gunshot, jackhammer, siren, and street music. First, understand what is going on here. So we need some way that can transform input text into numeric feature in a meaningful way. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. Additionally, we might want to extract new features by applying complex transformations on our original dataset. But still we have to calculate it first. Build a Docker image containing your code and any software dependencies with the appropriate control flow. Featuretools is an open source framework for automated feature engineering created by Feature Labs. Whether the feature should be made of word or character n-grams. NLTK doesn't include a paragraph tokenizer, so we'll try to create our own. You end up with a DataFrame extracted_features with all more than 1200 different extracted features. We develop an algorithm for feature extraction based on structural similarity and demonstrate its application for atom and pattern finding in high-resolution electron and scanning probe microscopy images. The sklearn. This is first of a two part blog on how to implement all this in python and understand the theoretical background and use cases behind it. I have used the following wrapper for convenient feature extraction in TensorFlow. And the best way to do that is Bag of Words. All the code,data and results for this blog are available on my GITHUB profile. for display or search), one intended usage of this library is for machine learning (feature extraction). One logic that may work is this: a paragraph is detected if there are consecutive newline characters. Note: We also have a video based course on NLP, covering Topic Modelling and its implementation in Python. Measuring Text Similarity in Python Published on May 15, >>> from sklearn. At that point, we still need to extract features from the JSON files before training any model. preprocessing import TransactionEncoder. Installation. 07/03/2019; 4 minutes to read +4; In this article. We use the trained (on ILSVRC-2012) inception v3 network available in TensorFlow to extract features from CIFAR-10 images. Built-in deep learning models. To install from pypi: pip install python_speech_features From this. Given 6000 40 X 40 photo patches taken out of 50 x-ray scans, what can be best way to extract useful features out of this patches? I need the method to: not be too computationally costly the latent. svm import LinearSVC from sklearn. This is an excerpt from the Python Data Science Handbook by Jake VanderPlas; Jupyter notebooks are available on GitHub. for classification), this library gives you plain text that you can later feed into a standard text classification pipeline. However, in most cases you will likely benefit from the feature extraction infrastructure that ClearTK provides to accomplish a. If you want to use the text of the html page as a feature (e. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: