{"id":288,"date":"2018-05-09T19:20:08","date_gmt":"2018-05-09T19:20:08","guid":{"rendered":"http:\/\/ai.intelligentonlinetools.com\/ml\/?p=288"},"modified":"2018-11-17T00:38:02","modified_gmt":"2018-11-17T00:38:02","slug":"text-classification-cnn-convolutional-neural-network-python","status":"publish","type":"post","link":"http:\/\/ai.intelligentonlinetools.com\/ml\/text-classification-cnn-convolutional-neural-network-python\/","title":{"rendered":"Text Classification of Different Datasets with CNN Convolutional Neural Network and Python"},"content":{"rendered":"<div class=\"zkgrq69efb800b1094\" ><script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\n<!-- Text analytics techniques 728_90 horizontal top -->\n<ins class=\"adsbygoogle\"\n     style=\"display:inline-block;width:728px;height:90px\"\n     data-ad-client=\"ca-pub-3416618249440971\"\n     data-ad-slot=\"2926649501\"><\/ins>\n<script>\n(adsbygoogle = window.adsbygoogle || []).push({});\n<\/script><\/div><style type=\"text\/css\">\r\n.zkgrq69efb800b1094 {\r\nmargin: 5px; padding: 0px;\r\n}\r\n@media screen and (min-width: 1201px) {\r\n.zkgrq69efb800b1094 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 993px) and (max-width: 1200px) {\r\n.zkgrq69efb800b1094 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 769px) and (max-width: 992px) {\r\n.zkgrq69efb800b1094 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 768px) and (max-width: 768px) {\r\n.zkgrq69efb800b1094 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (max-width: 767px) {\r\n.zkgrq69efb800b1094 {\r\ndisplay: block;\r\n}\r\n}\r\n<\/style>\r\n<p>In this post we explore machine learning text classification of 3 text datasets using CNN Convolutional Neural Network in Keras and python.  As reported on papers and blogs over the web, convolutional neural networks give good results in text classification.   <\/p>\n<h2>Datasets<\/h2>\n<p>We will use the following datasets:<br \/>\n1. 20 newsgroups text dataset  that is available from scikit learn <a href=\"http:\/\/scikit-learn.org\/stable\/datasets\/twenty_newsgroups.html\" target=\"_blank\">here<\/a>.<br \/>\n2. Dataset of web pages. The web documents are downloaded manually from web and belong to two categories : text mining or hidden markov models (HMM). This is small dataset that consists only of 20 pages for text mining and 11 pages for HMM group.<br \/>\n3. Datasets of tweets about Year Resolutions, obtained from data.world\/crowdflower <a href=\"https:\/\/data.world\/crowdflower\/2015-new-years-resolutions\" target=\"_blank\">here<\/a>.<\/p>\n<h2>Convolutional Neural Network Architecture<\/h2>\n<p>Our CNN will be based on Richard Liao code from [1], [2]. We use convolutional neural network that is built with different layers such as Embedding , Conv1D, Flatten, Dense. For embedding we utilize pretrained glove dataset that can be downloaded from web.<\/p>\n<p>The data flow diagram with layers used is shown below.<br \/>\n<figure id=\"attachment_299\" aria-describedby=\"caption-attachment-299\" style=\"width: 476px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" loading=\"lazy\" src=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/05\/CNN-diagram.png\" alt=\"CNN diagram\" width=\"486\" height=\"617\" class=\"size-full wp-image-299\" srcset=\"http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/05\/CNN-diagram.png 486w, http:\/\/ai.intelligentonlinetools.com\/ml\/wp-content\/uploads\/2018\/05\/CNN-diagram-236x300.png 236w\" sizes=\"(max-width: 486px) 100vw, 486px\" \/><figcaption id=\"caption-attachment-299\" class=\"wp-caption-text\">CNN diagram<\/figcaption><\/figure><\/p>\n<p>Here is the code for obtaining convolutional neural net diagram like this. Insert it after model.fit (&#8230;) line. It requires  installation of pydot and graphviz however. <\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\nmodel.fit(.....)\r\n\r\nimport pydot\r\npydot.find_graphviz = lambda: True\r\nprint (pydot.find_graphviz())\r\n\r\nimport os\r\nos.environ[&quot;PATH&quot;] += os.pathsep + &quot;C:\\\\Program Files (x86)\\\\Graphviz2.38\\\\bin&quot;\r\n\r\nfrom keras.utils import plot_model\r\nplot_model(model, to_file='model.png')\r\n<\/pre>\n<h2>1D Convolution<\/h2>\n<p>In our neural net convolution is performed in several 1 dimensional convolution layers (Conv1D)<br \/>\n1D convolution means that just 1-direction is used to calculate convolution.[3]<br \/>\nFor example:<br \/>\ninput = [1,1,1,1,1], filter = [0.25,0.5,0.25], output = [1,1,1,1,1]<br \/>\noutput-shape is 1D array<br \/>\nWe can also apply 1D convolution for 2D data matrix &#8211; as we use in text classification.<br \/>\nThe good explanation of convolution in text can be found in [6]<\/p>\n<h2>Text Classifiction of 20 Newsgroups Text Dataset<\/h2>\n<p>For this dataset we use only 2 categories. The script is provided <a href=\"http:\/\/ai.intelligentonlinetools.com\/ml\/text-classification-20-newsgroups-dataset-using-convolutional-neural-network\/\" target=\"_blank\">here<\/a>  The accuracy of network is 87%. Trained on 864 samples, validate on 215 samples.<br \/>\nSummary of run: loss: 0.6205 &#8211; acc: 0.6632 &#8211; val_loss: 0.5122 &#8211; val_acc: 0.8651<\/p>\n<h2>Document classification of Web Pages.<\/h2>\n<p>Here we use also 2 categories. Python script is provided <a href=\"http:\/\/ai.intelligentonlinetools.com\/ml\/document-classification-using-convolutional-neural-network\/\" target=\"_blank\">here<\/a>.<\/p>\n<p>Web page were manually downloaded from web and saved locally in two folders, one for each category. The script is loading web page files from locale storage. Next is preprocessing step  to remove web tags but keep text content. Here is the function for this:<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\ndef get_only_text_from_html_doc(page):\r\n &quot;&quot;&quot; \r\n  return the title and the text of the article\r\n &quot;&quot;&quot;\r\n \r\n soup = BeautifulSoup(page, &quot;lxml&quot;)\r\n text = ' '.join(map(lambda p: p.text, soup.find_all('p')))\r\n return soup.title.text + &quot; &quot; + text  \r\n<\/pre>\n<p>Accuracy on this dataset was 100% but was not consistent. In some other runs the result was only 83%.<br \/>\nTrained on 25 samples, validate on 6 samples.<br \/>\nSummary of run &#8211; loss: 0.0096 &#8211; acc: 1.0000 &#8211; val_loss: 0.0870 &#8211; val_acc: 1.0000<\/p>\n<h2>Text Classification of Tweet Dataset<\/h2>\n<p>The script is provided <a href=\"http:\/\/ai.intelligentonlinetools.com\/ml\/text-classification-tweet-dataset-using-convolutional-neural-network\/\" target=\"_blank\">here<\/a>.<br \/>\nHere is the accuracy was 93%. Trained on 4010 samples, validate on 1002 samples.<br \/>\nSummary of run &#8211; loss: 0.0193 &#8211; acc: 0.9958 &#8211; val_loss: 0.6690 &#8211; val_acc: 0.9281.<\/p>\n<h2>Conclusion<\/h2>\n<p>We learned how to do text classification for 3 different types of text datasets (Newsgroups, tweets, web documents). For text classification we used Convolutional Neural Network python and on all 3 datasets we got good performance on accuracy.  <\/p>\n<h2>References<\/h2>\n<p>1. <a href=\"https:\/\/richliao.github.io\/supervised\/classification\/2016\/11\/26\/textclassifier-convolutional\/\">Text Classification, Part I &#8211; Convolutional Networks<\/a><br \/>\n2. <a href=https:\/\/github.com\/richliao\/textClassifier\/blob\/master\/textClassifierConv.py target=\"_blank\">textClassifierConv<\/a><br \/>\n3. <a href=\"https:\/\/stackoverflow.com\/questions\/42883547\/what-do-you-mean-by-1d-2d-and-3d-convolutions-in-cnn\" target=\"_blank\">What do you mean by 1D, 2D and 3D Convolutions in CNN?<\/a><br \/>\n4.<a href=\"https:\/\/medium.com\/@thoszymkowiak\/how-to-implement-sentiment-analysis-using-word-embedding-and-convolutional-neural-networks-on-keras-163197aef623\" target=\"_blank\">How to implement Sentiment Analysis using word embedding and Convolutional Neural Networks on Keras.<\/a><br \/>\n5. <a href=\"http:\/\/www.wildml.com\/2015\/11\/understanding-convolutional-neural-networks-for-nlp\/\" target=\"_blank\">Understanding Convolutional Neural Networks for NLP<\/a><br \/>\n6. <a href=\"http:\/\/debajyotidatta.github.io\/nlp\/deep\/learning\/word-embeddings\/2016\/11\/27\/Understanding-Convolutions-In-Text\/\" target=\"_blank\">Understanding Convolutions in Text<\/a><br \/>\n7. <a href=\"https:\/\/pt.slideshare.net\/xavigiro\/recurrent-neural-networks-1-d2l2-deep-learning-for-speech-and-language-upc-2017?next_slideshow=1\" target=\"_blank\">Recurrent Neural Networks I <\/a><\/p>\n<div class=\"xbdhr69efb800b10c7\" ><center>\n<script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\n<!-- Text analytics techniques link ads horizontal Medium after content -->\n<ins class=\"adsbygoogle\"\n     style=\"display:inline-block;width:468px;height:15px\"\n     data-ad-client=\"ca-pub-3416618249440971\"\n     data-ad-slot=\"5765984772\"><\/ins>\n<script>\n(adsbygoogle = window.adsbygoogle || []).push({});\n<\/script>\n\n<script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\n<ins class=\"adsbygoogle\"\n     style=\"display:block\"\n     data-ad-format=\"autorelaxed\"\n     data-ad-client=\"ca-pub-3416618249440971\"\n     data-ad-slot=\"3903486841\"><\/ins>\n<script>\n     (adsbygoogle = window.adsbygoogle || []).push({});\n<\/script>\n<\/center><\/div><style type=\"text\/css\">\r\n.xbdhr69efb800b10c7 {\r\nmargin: 5px; padding: 0px;\r\n}\r\n@media screen and (min-width: 1201px) {\r\n.xbdhr69efb800b10c7 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 993px) and (max-width: 1200px) {\r\n.xbdhr69efb800b10c7 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 769px) and (max-width: 992px) {\r\n.xbdhr69efb800b10c7 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (min-width: 768px) and (max-width: 768px) {\r\n.xbdhr69efb800b10c7 {\r\ndisplay: block;\r\n}\r\n}\r\n@media screen and (max-width: 767px) {\r\n.xbdhr69efb800b10c7 {\r\ndisplay: block;\r\n}\r\n}\r\n<\/style>\r\n","protected":false},"excerpt":{"rendered":"<p>In this post we explore machine learning text classification of 3 text datasets using CNN Convolutional Neural Network in Keras and python. As reported on papers and blogs over the web, convolutional neural networks give good results in text classification. Datasets We will use the following datasets: 1. 20 newsgroups text dataset that is available &#8230; <a title=\"Text Classification of Different Datasets with CNN Convolutional Neural Network and Python\" class=\"read-more\" href=\"http:\/\/ai.intelligentonlinetools.com\/ml\/text-classification-cnn-convolutional-neural-network-python\/\" aria-label=\"More on Text Classification of Different Datasets with CNN Convolutional Neural Network and Python\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[34,33],"tags":[37,38,35,22,36],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Text Classification of Different Datasets with CNN Convolutional Neural Network and Python - Text Analytics Techniques<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/ai.intelligentonlinetools.com\/ml\/text-classification-cnn-convolutional-neural-network-python\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Text Classification of Different Datasets with CNN Convolutional Neural Network and Python - Text Analytics Techniques\" \/>\n<meta property=\"og:description\" content=\"In this post we explore machine learning text classification of 3 text datasets using CNN Convolutional Neural Network in Keras and python. 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