Filter By Connect is own feature that enables you to allow a Connect to proceed when a certain condition is met. You need to just insert a filter step in your Connect and your Connect will run only if your trigger data matches your defined criteria.
MonkeyLearn is a text analysis platform that helps you identify and extract actionable data from a variety of raw texts, including emails, chats, webpages, papers, tweets, and more! You can use custom tags to categorize texts, such as sentiments or topics, and extract specific data, such as organizations or keywords.Monkey Learn Integrations
Gmail + Filter By ConnectOnly continue if in Filter By Connect when New Attachment is created in Gmail Read More...
Gmail + Filter By ConnectOnly continue if in Filter By Connect when New Labeled Email is created in Gmail Read More...
Gmail + Filter By ConnectOnly continue if in Filter By Connect when New Email Matching Search is created in Gmail Read More...
Gmail + Filter By ConnectOnly continue if in Filter By Connect when New Starred Email is created in Gmail Read More...
Gmail + Filter By ConnectOnly continue if in Filter By Connect when New Thread is created in Gmail Read More...
It's easy to connect Filter By Connect + Monkey Learn without coding knowledge. Start creating your own business flow.
Filter By Connect is a software as a service (SaaS. that provides people with a new way to filter content online. It enables users to filter content by any topic of their choosing. Users can easily create a filter by connecting a few words on a web page, and then Filter By Connect will instantly filter all other sites on the web that also contain those words. The more times a word appears on a page, the more likely that page will be included in the results.
MonkeyLearn is a machine learning platform, where one can upload data and train a model using this data. It also offers APIs to make it easier to integrate machine learning into existing applications.
In this section, we will examine how to use Filter By Connect and Monkey Learn together to build a simple recipe finding application for finding recipes with specific ingredients and types of ingredients. This application will find recipes based on ingredient descriptions and tags. It will work with any recipe site.
To help our application find recipes by the ingredients they contain, we will use the Text Classification API from MonkeyLearn. This API allows us to upload text and classify it as belonging to one of the categories we specify. For example, we can use this API to classify a user-entered description of an ingredient as belonging to one of these categories:
categories = ['dessert', 'salad', 'soup']
To build the application, we will use two filters. one that finds recipes and another that finds recipes based on their tags.
To find recipes based on the ingredient description we enter, we will use Filter By Connect together with Monkey Learn’s Text Classification API. First, we would create a new filter using the “filter by connect” button (not available yet. and paste this URL. https://www.googleapis.com/chromium-browser-v42/extensions/lhgngmhkccbbjbdcpjfobdlecfeoedlbb/language_tops/filter_by_connect/text_classification?from=en&to=es&lang=es . Here, we are specifying Spanish as the language of the page we are trying to filter. We are doing this because Monkey Learn’s Text Classification API only supports English content at the moment.
Next, we would select “Classify Text” from the “Text” dropdown menu. This option allows us to classify text as belonging to one or more of our chosen categories. We just need to type in our ingredient description and click “Classify”. The result will incorporate our chosen language filter (in this case, Spanish.
The second filter will find recipes based on tags. To do this, I used 4 filters, which I connected to each other in series. These four filters are. 1. Find recipes tagged with “soup” using Google Images 2. Find recipes tagged with “tacos” using Google Images 3. Find recipes tagged with “soup” using Google Images 4. Find recipes tagged with “tacos” using Google Images. View the final working app here.
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