Found 151 repositories(showing 30)
dipanjanS
Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.
Masudbro94
Open in app Get started ITNEXT Published in ITNEXT You have 2 free member-only stories left this month. Sign up for Medium and get an extra one Kush Kush Follow Apr 15, 2021 · 7 min read · Listen Save How you can Control your Android Device with Python Photo by Caspar Camille Rubin on Unsplash Photo by Caspar Camille Rubin on Unsplash Introduction A while back I was thinking of ways in which I could annoy my friends by spamming them with messages for a few minutes, and while doing some research I came across the Android Debug Bridge. In this quick guide I will show you how you can interface with it using Python and how to create 2 quick scripts. The ADB (Android Debug Bridge) is a command line tool (CLI) which can be used to control and communicate with an Android device. You can do many things such as install apps, debug apps, find hidden features and use a shell to interface with the device directly. To enable the ADB, your device must firstly have Developer Options unlocked and USB debugging enabled. To unlock developer options, you can go to your devices settings and scroll down to the about section and find the build number of the current software which is on the device. Click the build number 7 times and Developer Options will be enabled. Then you can go to the Developer Options panel in the settings and enable USB debugging from there. Now the only other thing you need is a USB cable to connect your device to your computer. Here is what todays journey will look like: Installing the requirements Getting started The basics of writing scripts Creating a selfie timer Creating a definition searcher Installing the requirements The first of the 2 things we need to install, is the ADB tool on our computer. This comes automatically bundled with Android Studio, so if you already have that then do not worry. Otherwise, you can head over to the official docs and at the top of the page there should be instructions on how to install it. Once you have installed the ADB tool, you need to get the python library which we will use to interface with the ADB and our device. You can install the pure-python-adb library using pip install pure-python-adb. Optional: To make things easier for us while developing our scripts, we can install an open-source program called scrcpy which allows us to display and control our android device with our computer using a mouse and keyboard. To install it, you can head over to the Github repo and download the correct version for your operating system (Windows, macOS or Linux). If you are on Windows, then extract the zip file into a directory and add this directory to your path. This is so we can access the program from anywhere on our system just by typing in scrcpy into our terminal window. Getting started Now that all the dependencies are installed, we can start up our ADB and connect our device. Firstly, connect your device to your PC with the USB cable, if USB debugging is enabled then a message should pop up asking if it is okay for your PC to control the device, simply answer yes. Then on your PC, open up a terminal window and start the ADB server by typing in adb start-server. This should print out the following messages: * daemon not running; starting now at tcp:5037 * daemon started successfully If you also installed scrcpy, then you can start that by just typing scrcpy into the terminal. However, this will only work if you added it to your path, otherwise you can open the executable by changing your terminal directory to the directory of where you installed scrcpy and typing scrcpy.exe. Hopefully if everything works out, you should be able to see your device on your PC and be able to control it using your mouse and keyboard. Now we can create a new python file and check if we can find our connected device using the library: Here we import the AdbClient class and create a client object using it. Then we can get a list of devices connected. Lastly, we get the first device out of our list (it is generally the only one there if there is only one device connected). The basics of writing scripts The main way we are going to interface with our device is using the shell, through this we can send commands to simulate a touch at a specific location or to swipe from A to B. To simulate screen touches (taps) we first need to work out how the screen coordinates work. To help with these we can activate the pointer location setting in the developer options. Once activated, wherever you touch on the screen, you can see that the coordinates for that point appear at the top. The coordinate system works like this: A diagram to show how the coordinate system works A diagram to show how the coordinate system works The top left corner of the display has the x and y coordinates (0, 0) respectively, and the bottom right corners’ coordinates are the largest possible values of x and y. Now that we know how the coordinate system works, we need to check out the different commands we can run. I have made a list of commands and how to use them below for quick reference: Input tap x y Input text “hello world!” Input keyevent eventID Here is a list of some common eventID’s: 3: home button 4: back button 5: call 6: end call 24: volume up 25: volume down 26: turn device on or off 27: open camera 64: open browser 66: enter 67: backspace 207: contacts 220: brightness down 221: brightness up 277: cut 278: copy 279: paste If you wanted to find more, here is a long list of them here. Creating a selfie timer Now we know what we can do, let’s start doing it. In this first example I will show you how to create a quick selfie timer. To get started we need to import our libraries and create a connect function to connect to our device: You can see that the connect function is identical to the previous example of how to connect to your device, except here we return the device and client objects for later use. In our main code, we can call the connect function to retrieve the device and client objects. From there we can open up the camera app, wait 5 seconds and take a photo. It’s really that simple! As I said before, this is simply replicating what you would usually do, so thinking about how to do things is best if you do them yourself manually first and write down the steps. Creating a definition searcher We can do something a bit more complex now, and that is to ask the browser to find the definition of a particular word and take a screenshot to save it on our computer. The basic flow of this program will be as such: 1. Open the browser 2. Click the search bar 3. Enter the search query 4. Wait a few seconds 5. Take a screenshot and save it But, before we get started, you need to find the coordinates of your search bar in your default browser, you can use the method I suggested earlier to find them easily. For me they were (440, 200). To start, we will have to import the same libraries as before, and we will also have our same connect method. In our main function we can call the connect function, as well as assign a variable to the x and y coordinates of our search bar. Notice how this is a string and not a list or tuple, this is so we can easily incorporate the coordinates into our shell command. We can also take an input from the user to see what word they want to get the definition for: We will add that query to a full sentence which will then be searched, this is so that we can always get the definition. After that we can open the browser and input our search query into the search bar as such: Here we use the eventID 66 to simulate the press of the enter key to execute our search. If you wanted to, you could change the wait timings per your needs. Lastly, we will take a screenshot using the screencap method on our device object, and we can save that as a .png file: Here we must open the file in the write bytes mode because the screencap method returns bytes representing the image. If all went according to plan, you should have a quick script which searches for a specific word. Here it is working on my phone: A GIF to show how the definition searcher example works on my phone A GIF to show how the definition searcher example works on my phone Final thoughts Hopefully you have learned something new today, personally I never even knew this was a thing before I did some research into it. The cool thing is, that you can do anything you normal would be able to do, and more since it just simulates your own touches and actions! I hope you enjoyed the article and thank you for reading! 💖 468 9 468 9 More from ITNEXT Follow ITNEXT is a platform for IT developers & software engineers to share knowledge, connect, collaborate, learn and experience next-gen technologies. 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Orbifold
Knwler is a lightweight, single-file Python tool that extracts structured knowledge graphs from documents using AI. Feed it a PDF or text file and receive a richly connected network of entities, relationships, and topics — complete with an interactive HTML report and exports ready for your favorite graph analytics platform.
Source Code for 'Text Analytics with Python,' 2nd Edition by Dipanjan Sarkar
Apress
Source code for 'Text Analytics with Python' by Dipanjan Sarkar
hiteshsuthar01
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for="radio_darkcode"> Dark code</label> </div> <nav id="nav_tutorials" class="w3-hide-small" style="position: absolute; padding-bottom: 60px; display: none;"> <div class="w3-content" style="max-width:1100px;font-size:18px"> <span onclick="w3_close_nav('tutorials')" class="w3-button w3-xxxlarge w3-display-topright w3-hover-white sectionxsclosenavspan" style="padding-right:30px;padding-left:30px;">×</span><br> <div class="w3-row-padding w3-bar-block"> <div class="w3-container" style="padding-left:13px"> <h2 style="color:#FFF4A3;"><b>Tutorials</b></h2> </div> <div class="w3-col l3 m6"> <h3 class="w3-margin-top">HTML and CSS</h3> <a class="w3-bar-item w3-button" href="/html/default.asp">Learn HTML</a> <a class="w3-bar-item w3-button" href="/css/default.asp">Learn CSS</a> <a class="w3-bar-item w3-button" href="/css/css_rwd_intro.asp" title="Responsive Web Design">Learn RWD</a> <a class="w3-bar-item w3-button" href="/bootstrap/bootstrap_ver.asp">Learn Bootstrap</a> <a class="w3-bar-item w3-button" href="/w3css/default.asp">Learn W3.CSS</a> <a class="w3-bar-item w3-button" href="/colors/default.asp">Learn Colors</a> <a class="w3-bar-item w3-button" href="/icons/default.asp">Learn Icons</a> <a class="w3-bar-item w3-button" href="/graphics/default.asp">Learn Graphics</a> <a class="w3-bar-item w3-button" href="/graphics/svg_intro.asp">Learn SVG</a> <a class="w3-bar-item w3-button" href="/graphics/canvas_intro.asp">Learn Canvas</a> <a class="w3-bar-item w3-button" href="/howto/default.asp">Learn How To</a> <a class="w3-bar-item w3-button" href="/sass/default.php">Learn Sass</a> <div class="w3-hide-large w3-hide-small"> <h3 class="w3-margin-top">Data Analytics</h3> <a class="w3-bar-item w3-button" href="/ai/default.asp">Learn AI</a> <a class="w3-bar-item w3-button" href="/python/python_ml_getting_started.asp">Learn Machine Learning</a> <a class="w3-bar-item w3-button" href="/datascience/default.asp">Learn Data Science</a> <a class="w3-bar-item w3-button" href="/python/numpy/default.asp">Learn NumPy</a> <a class="w3-bar-item w3-button" href="/python/pandas/default.asp">Learn Pandas</a> <a class="w3-bar-item w3-button" href="/python/scipy/index.php">Learn SciPy</a> <a class="w3-bar-item w3-button" href="/python/matplotlib_intro.asp">Learn Matplotlib</a> <a class="w3-bar-item w3-button" href="/statistics/index.php">Learn Statistics</a> <a class="w3-bar-item w3-button" href="/excel/index.php">Learn Excel</a> <h3 class="w3-margin-top">XML Tutorials</h3> <a class="w3-bar-item w3-button" href="/xml/default.asp">Learn XML</a> <a class="w3-bar-item w3-button" href="/xml/ajax_intro.asp">Learn XML AJAX</a> <a class="w3-bar-item w3-button" href="/xml/dom_intro.asp">Learn XML DOM</a> <a class="w3-bar-item w3-button" href="/xml/xml_dtd_intro.asp">Learn XML DTD</a> <a class="w3-bar-item w3-button" href="/xml/schema_intro.asp">Learn XML Schema</a> <a class="w3-bar-item w3-button" href="/xml/xsl_intro.asp">Learn XSLT</a> <a class="w3-bar-item w3-button" href="/xml/xpath_intro.asp">Learn XPath</a> <a class="w3-bar-item w3-button" href="/xml/xquery_intro.asp">Learn XQuery</a> </div> </div> <div class="w3-col l3 m6"> <h3 class="w3-margin-top">JavaScript</h3> <a class="w3-bar-item w3-button" href="/js/default.asp">Learn JavaScript</a> <a class="w3-bar-item w3-button" href="/jquery/default.asp">Learn jQuery</a> <a class="w3-bar-item w3-button" href="/react/default.asp">Learn React</a> <a class="w3-bar-item w3-button" href="/angular/default.asp">Learn AngularJS</a> <a class="w3-bar-item w3-button" href="/js/js_json_intro.asp">Learn JSON</a> <a class="w3-bar-item w3-button" href="/js/js_ajax_intro.asp">Learn AJAX</a> <a class="w3-bar-item w3-button" href="/appml/default.asp">Learn AppML</a> <a class="w3-bar-item w3-button" href="/w3js/default.asp">Learn W3.JS</a> <h3 class="w3-margin-top">Programming</h3> <a class="w3-bar-item w3-button" href="/python/default.asp">Learn Python</a> <a class="w3-bar-item w3-button" href="/java/default.asp">Learn Java</a> <a class="w3-bar-item w3-button" href="/c/index.php">Learn C</a> <a class="w3-bar-item w3-button" href="/cpp/default.asp">Learn C++</a> <a class="w3-bar-item w3-button" href="/cs/index.php">Learn C#</a> <a class="w3-bar-item w3-button" href="/r/default.asp">Learn R</a> <a class="w3-bar-item w3-button" href="/kotlin/index.php">Learn Kotlin</a> <a class="w3-bar-item w3-button" href="/go/index.php">Learn Go</a> <a class="w3-bar-item w3-button" href="/django/index.php">Learn Django</a> <a class="w3-bar-item w3-button" href="/typescript/index.php">Learn TypeScript</a> </div> <div class="w3-col l3 m6"> <h3 class="w3-margin-top">Server Side</h3> <a class="w3-bar-item w3-button" href="/sql/default.asp">Learn SQL</a> <a class="w3-bar-item w3-button" href="/mysql/default.asp">Learn MySQL</a> <a class="w3-bar-item w3-button" href="/php/default.asp">Learn PHP</a> <a class="w3-bar-item w3-button" href="/asp/default.asp">Learn ASP</a> <a class="w3-bar-item w3-button" href="/nodejs/default.asp">Learn Node.js</a> <a class="w3-bar-item w3-button" href="/nodejs/nodejs_raspberrypi.asp">Learn Raspberry Pi</a> <a class="w3-bar-item w3-button" href="/git/default.asp">Learn Git</a> <a class="w3-bar-item w3-button" href="/aws/index.php">Learn AWS Cloud</a> <h3 class="w3-margin-top">Web Building</h3> <a class="w3-bar-item w3-button" href="https://www.w3schools.com/spaces" target="_blank" onclick="ga('send', 'event', 'spacesFromTutorialsAcc', 'click');" title="Get Your Own Website With W3schools Spaces">Create a Website <span class="ribbon-topnav ws-yellow">NEW</span></a> <a class="w3-bar-item w3-button" href="/where_to_start.asp">Where To Start</a> <a class="w3-bar-item w3-button" href="/w3css/w3css_templates.asp">Web Templates</a> <a class="w3-bar-item w3-button" href="/browsers/default.asp">Web Statistics</a> <a class="w3-bar-item w3-button" href="/cert/default.asp">Web Certificates</a> <a class="w3-bar-item w3-button" href="/whatis/default.asp">Web Development</a> <a class="w3-bar-item w3-button" href="/tryit/default.asp">Code Editor</a> <a class="w3-bar-item w3-button" href="/typingspeed/default.asp">Test Your Typing Speed</a> <a class="w3-bar-item w3-button" href="/codegame/index.html" target="_blank">Play a Code Game</a> <a class="w3-bar-item w3-button" href="/cybersecurity/index.php">Cyber Security</a> <a class="w3-bar-item w3-button" href="/accessibility/index.php">Accessibility</a> </div> <div class="w3-col l3 m6 w3-hide-medium"> <h3 class="w3-margin-top">Data Analytics</h3> <a class="w3-bar-item w3-button" href="/ai/default.asp">Learn AI</a> <a class="w3-bar-item w3-button" href="/python/python_ml_getting_started.asp">Learn Machine Learning</a> <a class="w3-bar-item w3-button" href="/datascience/default.asp">Learn Data Science</a> <a class="w3-bar-item w3-button" href="/python/numpy/default.asp">Learn NumPy</a> <a class="w3-bar-item w3-button" href="/python/pandas/default.asp">Learn Pandas</a> <a class="w3-bar-item w3-button" href="/python/scipy/index.php">Learn SciPy</a> <a class="w3-bar-item w3-button" href="/python/matplotlib_intro.asp">Learn Matplotlib</a> <a class="w3-bar-item w3-button" href="/statistics/index.php">Learn Statistics</a> <a class="w3-bar-item w3-button" href="/excel/index.php">Learn Excel</a> <a class="w3-bar-item w3-button" href="/googlesheets/index.php">Learn Google Sheets</a> <h3 class="w3-margin-top">XML Tutorials</h3> <a class="w3-bar-item w3-button" href="/xml/default.asp">Learn XML</a> <a class="w3-bar-item w3-button" href="/xml/ajax_intro.asp">Learn XML AJAX</a> <a class="w3-bar-item w3-button" href="/xml/dom_intro.asp">Learn XML DOM</a> <a class="w3-bar-item w3-button" href="/xml/xml_dtd_intro.asp">Learn XML DTD</a> <a class="w3-bar-item w3-button" href="/xml/schema_intro.asp">Learn XML Schema</a> <a class="w3-bar-item w3-button" href="/xml/xsl_intro.asp">Learn XSLT</a> <a class="w3-bar-item w3-button" href="/xml/xpath_intro.asp">Learn XPath</a> <a class="w3-bar-item w3-button" href="/xml/xquery_intro.asp">Learn XQuery</a> </div> </div> </div> <br class="hidesm"> </nav> <nav id="nav_references" class="w3-hide-small" style="position: absolute; padding-bottom: 60px; display: none;"> <div class="w3-content" style="max-width:1100px;font-size:18px"> <span onclick="w3_close_nav('references')" class="w3-button w3-xxxlarge w3-display-topright w3-hover-white sectionxsclosenavspan" style="padding-right:30px;padding-left:30px;">×</span><br> <div class="w3-row-padding w3-bar-block"> <div class="w3-container" style="padding-left:13px"> <h2 style="color:#FFF4A3;"><b>References</b></h2> </div> <div class="w3-col l3 m6"> <h3 class="w3-margin-top">HTML</h3> <a class="w3-bar-item w3-button" href="/tags/default.asp">HTML Tag Reference</a> <a class="w3-bar-item w3-button" href="/tags/ref_html_browsersupport.asp">HTML Browser Support</a> <a class="w3-bar-item w3-button" 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<a target="_top" href="tag_thead.asp"><thead></a> <a target="_top" href="tag_time.asp"><time></a> <a target="_top" href="tag_title.asp"><title></a> <a target="_top" href="tag_tr.asp"><tr></a> <a target="_top" href="tag_track.asp"><track></a> <a target="_top" href="tag_tt.asp"><tt></a> <a target="_top" href="tag_u.asp"><u></a> <a target="_top" href="tag_ul.asp"><ul></a> <a target="_top" href="tag_var.asp"><var></a> <a target="_top" href="tag_video.asp"><video></a> <a target="_top" href="tag_wbr.asp"><wbr></a> </div> <br><br> </div> </div> </div> <div class="w3-main w3-light-grey" id="belowtopnav" style="margin-left: 220px; padding-top: 44px;"> <div class="w3-row w3-white"> <div class="w3-col l10 m12" id="main"> <div id="mainLeaderboard" style="overflow:hidden;"> <!-- MainLeaderboard--> <!--<pre>main_leaderboard, all: [728,90][970,90][320,50][468,60]</pre>--> <div id="adngin-main_leaderboard-0" data-google-query-id="CJPA_sueqvcCFXiOSwUd2fYBLg"><div 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Previous</span></a> <a class="w3-btn" href="default.asp"><span class="w3-hide-small">Complete HTML </span>Reference</a> <a class="w3-right w3-btn" href="tag_param.asp"><span class="w3-hide-small">Next </span>❯</a> </div> <br> <div class="w3-example"> <h3>Example</h3> <p>A paragraph is marked up as follows:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs1" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <p>More "Try it Yourself" examples below.</p> <hr> <h2>Definition and Usage</h2> <p>The <code class="w3-codespan"><p></code> tag defines a paragraph.</p> <p>Browsers automatically add a single blank line before and after each <code class="w3-codespan"><p></code> element.</p> <p><strong>Tip:</strong> Use CSS to <a href="/html/html_css.asp">style paragraphs</a>.</p> <hr> <h2>Browser Support</h2> <table class="browserref notranslate"> <tbody><tr> <th style="width:20%;font-size:16px;text-align:left;">Element</th> <th style="width:16%;" class="bsChrome" title="Chrome"></th> <th style="width:16%;" class="bsEdge" title="Internet Explorer / Edge"></th> <th style="width:16%;" class="bsFirefox" title="Firefox"></th> <th style="width:16%;" class="bsSafari" title="Safari"></th> <th style="width:16%;" class="bsOpera" title="Opera"></th> </tr><tr> <td style="text-align:left;"><p></td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> </tr> </tbody></table> <hr> <h2>Global Attributes</h2> <p>The <code class="w3-codespan"><p></code> tag also supports the <a href="ref_standardattributes.asp">Global Attributes in HTML</a>.</p> <hr> <h2>Event Attributes</h2> <p>The <code class="w3-codespan"><p></code> tag also supports the <a href="ref_eventattributes.asp">Event Attributes in HTML</a>.</p> <hr> <div id="midcontentadcontainer" style="overflow:auto;text-align:center"> <!-- MidContent --> <!-- <p class="adtext">Advertisement</p> --> <div id="adngin-mid_content-0" data-google-query-id="CKfs_8ueqvcCFXiOSwUd2fYBLg"><div id="sn_ad_label_adngin-mid_content-0" class="sn_ad_label" style="color:#000000;font-size:12px;margin:0;text-align:center;">ADVERTISEMENT</div><div id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1__container__" style="border: 0pt none; display: inline-block; width: 300px; height: 250px;"><iframe frameborder="0" src="https://56d0da6c34aaa471db22bb4266aac656.safeframe.googlesyndication.com/safeframe/1-0-38/html/container.html" id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1" title="3rd party ad content" name="" scrolling="no" marginwidth="0" marginheight="0" width="300" height="250" data-is-safeframe="true" sandbox="allow-forms allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-top-navigation-by-user-activation" role="region" aria-label="Advertisement" tabindex="0" data-google-container-id="8" style="border: 0px; vertical-align: bottom;" data-load-complete="true"></iframe></div></div> </div> <hr> <h2>More Examples</h2> <div class="w3-example"> <h3>Example</h3> <p>Align text in a paragraph (with CSS):</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="attributecolor" style="color:red"> style<span class="attributevaluecolor" style="color:mediumblue">="text-align:right"</span></span><span class="tagcolor" style="color:mediumblue">></span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_align_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Style paragraphs with CSS:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>html<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>head<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>style<span class="tagcolor" style="color:mediumblue">></span></span><span class="cssselectorcolor" style="color:brown"><br>p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br> color<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> navy<span class="cssdelimitercolor" style="color:black">;</span></span><br> text-indent<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 30px<span class="cssdelimitercolor" style="color:black">;</span></span><br> text-transform<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> uppercase<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span><br></span><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/style<span class="tagcolor" style="color:mediumblue">></span></span><br> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/head<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>body<span class="tagcolor" style="color:mediumblue">></span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/body<span class="tagcolor" style="color:mediumblue">></span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/html<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_style_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p> More on paragraphs:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span><br>This paragraph<br>contains a lot of lines<br>in the source code,<br> but the browser <br>ignores it.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs2" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Poem problems in HTML:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>p<span class="tagcolor" style="color:mediumblue">></span></span><br>My Bonnie lies over the ocean.<br>My Bonnie lies over the sea.<br>My Bonnie lies over the ocean.<br>Oh, bring back my Bonnie to me.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue"><</span>/p<span class="tagcolor" style="color:mediumblue">></span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_poem" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <hr> <h2>Related Pages</h2> <p>HTML tutorial: <a href="/html/html_paragraphs.asp">HTML Paragraphs</a></p> <p>HTML DOM reference: <a href="/jsref/dom_obj_paragraph.asp">Paragraph Object</a></p> <hr> <h2>Default CSS Settings</h2> <p>Most browsers will display the <code class="w3-codespan"><p></code> element with the following default values:</p> <div class="w3-example"> <h3>Example</h3> <div class="w3-code notranslate cssHigh"><span class="cssselectorcolor" style="color:brown"> p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br> display<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> block<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-top<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-bottom<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-left<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br> margin-right<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span> </span></div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_default_css" class="w3-btn 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neostrange
A Python framework for automating domain-agnostic and domain-specific Knowledge Graphs from unstructured text. Integrates NLP and Neo4j for entity extraction, relationship mapping, and semantic enrichment. Ideal for text mining and analytics, with support for temporal and event tagging.
AmirhosseinHonardoust
An AI-powered Fake Review Detector built with Python, Streamlit, and Scikit-learn. Uses TF-IDF vectorization, Logistic Regression, and behavioral text analytics (sentiment, exclamations, clichés) to identify synthetic or spammy product reviews. Includes training scripts and a full interactive dashboard.
This is demo repo to demostrate how to scrape post data from Facebook by Python with library facebook_scraper. And then use Azure Text Analytics to perform sentiment analysis for post text content.
Jai-Agarwal-04
Sentiment Analysis with Insights using NLP and Dash This project show the sentiment analysis of text data using NLP and Dash. I used Amazon reviews dataset to train the model and further scrap the reviews from Etsy.com in order to test my model. Prerequisites: Python3 Amazon Dataset (3.6GB) Anaconda How this project was made? This project has been built using Python3 to help predict the sentiments with the help of Machine Learning and an interactive dashboard to test reviews. To start, I downloaded the dataset and extracted the JSON file. Next, I took out a portion of 7,92,000 reviews equally distributed into chunks of 24000 reviews using pandas. The chunks were then combined into a single CSV file called balanced_reviews.csv. This balanced_reviews.csv served as the base for training my model which was filtered on the basis of review greater than 3 and less than 3. Further, this filtered data was vectorized using TF_IDF vectorizer. After training the model to a 90% accuracy, the reviews were scrapped from Etsy.com in order to test our model. Finally, I built a dashboard in which we can check the sentiments based on input given by the user or can check the sentiments of reviews scrapped from the website. What is CountVectorizer? CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for using in further text analysis). CountVectorizer creates a matrix in which each unique word is represented by a column of the matrix, and each text sample from the document is a row in the matrix. The value of each cell is nothing but the count of the word in that particular text sample. What is TF-IDF Vectorizer? TF-IDF stands for Term Frequency - Inverse Document Frequency and is a statistic that aims to better define how important a word is for a document, while also taking into account the relation to other documents from the same corpus. This is performed by looking at how many times a word appears into a document while also paying attention to how many times the same word appears in other documents in the corpus. The rationale behind this is the following: a word that frequently appears in a document has more relevancy for that document, meaning that there is higher probability that the document is about or in relation to that specific word a word that frequently appears in more documents may prevent us from finding the right document in a collection; the word is relevant either for all documents or for none. Either way, it will not help us filter out a single document or a small subset of documents from the whole set. So then TF-IDF is a score which is applied to every word in every document in our dataset. And for every word, the TF-IDF value increases with every appearance of the word in a document, but is gradually decreased with every appearance in other documents. What is Plotly Dash? Dash is a productive Python framework for building web analytic applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python. Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready. Dash is an open source library, released under the permissive MIT license. Plotly develops Dash and offers a platform for managing Dash apps in an enterprise environment. What is Web Scrapping? Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Running the project Step 1: Download the dataset and extract the JSON data in your project folder. Make a folder filtered_chunks and run the data_extraction.py file. This will extract data from the JSON file into equal sized chunks and then combine them into a single CSV file called balanced_reviews.csv. Step 2: Run the data_cleaning_preprocessing_and_vectorizing.py file. This will clean and filter out the data. Next the filtered data will be fed to the TF-IDF Vectorizer and then the model will be pickled in a trained_model.pkl file and the Vocabulary of the trained model will be stored as vocab.pkl. Keep these two files in a folder named model_files. Step 3: Now run the etsy_review_scrapper.py file. Adjust the range of pages and product to be scrapped as it might take a long long time to process. A small sized data is sufficient to check the accuracy of our model. The scrapped data will be stored in csv as well as db file. Step 4: Finally, run the app.py file that will start up the Dash server and we can check the working of our model either by typing or either by selecting the preloaded scrapped reviews.
This is demo repo to demostrate how to scrape apps review data from Google Play Store by Python with library Google-Play-Scraper. And then use Azure Text Analytics to perform sentiment analysis for reviews content (aka comments).
Here’s one such proxy site that you can build for your friends in China or even for your personal use (say for accessing blocked sites from office). This is created using Google App Engine and, contrary to what you may think, the setup is quite simple. 1. Go to appengine.google.com and sign-in using your Google Account. 2. Click the “Create an Application” button. Since this is your first time, Google will send a verification code via SMS to your mobile phone number. Type the code and you’re all set to create apps with Google App Engine. 3. Pick an Application Identifier and it becomes the sub-domain* of your proxy server. Give your app a title (say Proxy Server), set the Authentication Option as “Open to all users”, agree to the terms and create the application. (screenshot) 4. OK, now that we have reserved the APP ID, it’s time to create and upload the proxy server application to Google App Engine. Go to python.org, download the 2.7 Installer and install Python. If you are on Mac, Python 2.7 is already installed on your computer. 5. Download this zip file and extract it to your desktop. The zip file contains a couple of HTML, YAML and Python (.py) files that you can view inside WordPad. 6. Go to code.google.com, download the Google App Engine SDK for Python and follow the wizard to install the SDK on your computer. When the installation wizard has finished, click the “Run Launcher” button to open the App Engine Program. 7. Choose Edit -> Preferences inside the Google App Engine Launcher program from the desktop and set the correct values (see screenshot) for the Python Path, App Engine SDK and the Text Editor (set this is as WordPad or write.exe and not notepad.exe). 8. Click File – > Add Existing Application under the Google App Launcher program and browse to the folder that contain the index.yaml and other files that you extracted in Step 5. Once the project is added to App Engine, select the project and click Edit to replace “YOUR_APP_ID” with your App ID (screenshot). Save and close the file. 9. Click Deploy, enter you Google account credentials and, within a minute or two, your online proxy server will be deployed and become ready for use (screenshot). The public URL (or web address) of your new proxy server will be your_app_id.appspot.com (replace your_app_id with your App Engine Identifier). [*] The sub-domain or the App ID will uniquely identify your App Engine application. For this example, we’ll use labnol-proxy-server as the Application Identifier though you are free to choose any other unique name. Next Steps – Setting up a Free Proxy with Google You can edit the main.html file to change the appearance of your proxy website. You can even add code for Google Analytics and Google AdSense code to monetize your proxy server. The proxy server is public on the web (open to everyone) but you can add a layer of authentication so that only Google Account users who are logged-in can use your proxy server. If you have made any changes to your HTML files, you can upload the latest version to Google App Engine either by clicking the “Deploy” button again or use the following command – appcfg.py update <app-directory> This proxy works with Flash videos (like YouTube and ABC News) though not with Hulu. As some of you have suggested, web domains with the word “proxy” or “proxies” are banned at workplaces so you may avoid using them in your appspot.com proxy address. Though there exist proxy servers for accessing secure (https) sites, this is a basic proxy server that won’t work with sites that require logins (like Gmail). The proxy server code is available on Github and is fork of the Mirrorr project.
GTLibraryDataVisualization
No description available
amcquistan
Python based text analytics app with Flask and TextBlob
ikivanc
Here is my first sample with Azure Functions with Python using NTLK package for Text Analytics
johnlaudun
the briefest of introductions to text analytics with Python
dipanjanS
This is a temporary repository for working on improvements for my book 'Text Analytics with Python'
mmalonehallberg
Building classification models to predict grape varieties based on reviews; with help from Text Analytics with Python by Dipanjan Sarkar: https://github.com/dipanjanS/text-analytics-with-python)
Architectshwet
A project about the brief implementation of MLP, CNN, RNN, Unsupervised learning with Autoencoders, ETC, Text analytics using CNN in Python
shaynley
This is a walkthrough of basic text analytics in Python, working with an excerpt from a cookbook from 1871. (Covers text data cleaning and pre-processing, stop word removal, tokenization, lemmatization vs. stemming, word frequency, visualizations, as well as lexical density and part-of-speech (POS) distribution).
randallscott25
The Applied Data Science program at Syracuse University's School of Information Studies provides students the opportunity to collect, manage, analyze, and develop insights using data from a multitude of domains using various tools and techniques. In courses such as Database Administration, Data Analytics, Text Mining, and Marketing Analytics, reports and presentations were developed to deliver insights using Microsoft Access, SQL Server Management Studio, Python, R, Excel, and Tableau. The skills developed at the School of Information Studies furnish data scientists focused in the field of marketing analytics with the ability to generate value within their organizations and produce actionable recommendations.
This module aims to introduce students to the power of big data analytics and data visualisation techniques in contributing to business value creation. It will also enable students to solve a variety of complex data centred business problems using computer software tools like Python. The module covers two main themes: 1. Theoretical understanding of big data analytics: This part involves learning about the theoretical foundations of big data analytics, text mining, and social media data mining. It also introduces the effective use of data visualisation and database management concepts and their links with big data analytics. Example applications of big data analytics and visualisation techniques discussed within the module will focus on addressing contemporary challenges faced by industry. 2. Building practical skills and managerial insights: In this part of the module, students will learn how to solve business problems using advanced functions in Python. Students will be guided through demonstrations involving a variety of exercises that will prepare them to be data-driven managers and executives capable of utilising big data analytics for business value creation.
chomskim
APress, "Text Analytics with Python" Book, Source in IPython
alextanhongpin
From the book of the same title
In this notebook, we explore more advanced machine learning techniques with text data. First, we further explore the text classification we did in the previous lesson to see how we can improve. Next, we introduce the concept of n-grams, which are combinations of one or more tokens. For example, bigrams are combinations of two tokens, while trigrams are combinations of three tokens. Finally, we introdce sentiment analysis with a new text data set.
Data and Text Analytics with Python
iuliaferoli
Text analytics modules examples in python with translator, sentiment, key phrases
DaveOnData
Repository for the TDWI 1-Day course "Introduction to Text Analytics with Python"
jyotijauhari
Experimenting with python libraries, projects while learning from Text Data Analysis of Data Analytics Course
nmryan
An Alteryx tool for text analytics, written with the Python SDK. Built with lots of help from "Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning" by Benjamin Bengfort, Rebecca Bilbro, and Tony Ojeda.