![]() ![]() ![]() #Python feed reader proAs entry is a dictionary we utilize its keys to produce the values needed.Ĭontroversy erupted on Friday over the appointment of BJP MLA K G Bopaiah as pro tem speaker for the assembly, with Congress and JD(S) claiming the move went against convention that the post should go to the most senior member of the House. Post Title : Cong-JD(S) in SC over choice of pro tem speakerīased on above entry structure we can derive the necessary details from the feed using python program as shown below. When we run the above program we get the following output − Print 'Number of RSS posts :', len(NewsFeed.entries) In the below example we read the title and head of the rss feed. When we run the above program, we get the following output − In the below example we get the structure of the feed so that we can analyze further about which parts of the feed we want to process. In python we take help of the below package to read and process these feeds. Many news-related sites, weblogs and other online publishers syndicate their content as an RSS Feed to whoever wants it. Many news-related sites, weblogs and other online publishers syndicate their content as an RSS Feed to whoever wants it. RSS (Rich Site Summary) is a format for delivering regularly changing web content. The redirect URL can be anything.RSS (Rich Site Summary) is a format for delivering regularly changing web content. It requires that you input your consumer key and a redirect URL. Once this is set, you are ready to move on the the next step, which is to set up the authorizations. Python - Read blob object in python using wand library. This will look like the following screen, but obviously with a real key: Build an Application to extract news from Google News Feed Using Python. You can find this in the upper left-hand corner under My Apps. Once you have filled this in and submitted it, you will receive your CONSUMER KEY. Make sure to click all of the permissions so that you can add, change, and retrieve articles. Click on Create New App in the upper left-hand side and fill in the details to get your API key. You can sign up for an account at /developer/apps/new. Now that you’ve diligently saved your articles to Pocket, the next step is to retrieve them. To accomplish this, we’ll use the Pocket API. If you forget to tag an article when you save it, you can always go to the site,, to tag it there. Your end results will only be as good as your training set, so you’re going to to need to do this for hundreds of articles. Tag the interesting ones with “y”, and the non-interesting ones with “n”. ![]() Now comes the fun part! Begin saving all articles that you come across. When the icon is clicked, it turns red to indicated the article has been saved. The greyed out icon can be seen in the upper right-hand corner. It will turn red once the article has been saved as seen in the following images. It will be greyed out, but once there is an article you wish to save, you can click on it. ![]() Once this is complete, you should see the Pocket icon in the upper right-hand corner of your browser. If you already have an account, log in, and if you do not have an account, go ahead and sign up (it’s free). For Chrome, go into the Google App Store and look for the Extensions section:Ĭlick on the blue Add to Chrome button. We use Google Chrome here, but other browsers should work similarly. We’ll use this feature to mark interesting articles as “y” and non-interesting articles as “n”. One of the great features of Pocket for our purposes is itsĪbility to save the article with a tag of your choosing. The article is saved to your personal repository. #Python feed reader installYou simply install the browser extension, and then click on the Pocket icon in your browser’s toolbar when you wish to save a story. Pocket is an application that allows you to save stories to read later. To simplify this process, we will use the Pocket app. This will indicate whether the article is the one that we would want to have sent to us in our daily digest or not. For each article, we’ll label it either “y” or “n”. To build this corpus, we will need to annotate a large number of articles that correspond to these interests. This training data will be fed into our model in order to teach it to discriminate between the articles that we’d be interested in and the ones that we would not. Before we can create a model of our taste in news articles, we need training data. ![]()
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