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Integrate Monkey Learn with Facebook Groups

Appy Pie Connect allows you to automate multiple workflows between Monkey Learn and Facebook Groups

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About Monkey Learn

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.

About Facebook Groups

Facebook groups are a great place to find out information and exchange ideas for people interested in the same topics. It also serves as a forum for discussion and feedback.

Facebook Groups Integrations

Best ways to Integrate Monkey Learn + Facebook Groups

  • Monkey Learn Integration Monkey Learn Integration

    Facebook Groups + Monkey Learn

    Classify Text in monkeylearn when New Post is created in Facebook Groups Read More...
    Close
    When this happens...
    Monkey Learn Integration New Post
     
    Then do this...
    Monkey Learn Integration Classify Text
  • Monkey Learn Integration Monkey Learn Integration

    Facebook Groups + Monkey Learn

    Extract Text in monkeylearn when New Post is created in Facebook Groups Read More...
    Close
    When this happens...
    Monkey Learn Integration New Post
     
    Then do this...
    Monkey Learn Integration Extract Text
  • Monkey Learn Integration Monkey Learn Integration

    Facebook Groups + Monkey Learn

    Upload training Data in monkeylearn when New Post is created in Facebook Groups Read More...
    Close
    When this happens...
    Monkey Learn Integration New Post
     
    Then do this...
    Monkey Learn Integration Upload training Data
  • Monkey Learn Integration Monkey Learn Integration

    Facebook Groups + Monkey Learn

    Classify Text in monkeylearn when New Event is created in Facebook Groups Read More...
    Close
    When this happens...
    Monkey Learn Integration New Event
     
    Then do this...
    Monkey Learn Integration Classify Text
  • Monkey Learn Integration Monkey Learn Integration

    Facebook Groups + Monkey Learn

    Extract Text in monkeylearn when New Event is created in Facebook Groups Read More...
    Close
    When this happens...
    Monkey Learn Integration New Event
     
    Then do this...
    Monkey Learn Integration Extract Text
  • Monkey Learn Integration {{item.actionAppName}} Integration

    Monkey Learn + {{item.actionAppName}}

    {{item.message}} Read More...
    Close
    When this happens...
    {{item.triggerAppName}} Integration {{item.triggerTitle}}
     
    Then do this...
    {{item.actionAppName}} Integration {{item.actionTitle}}
Connect Monkey Learn + Facebook Groups in easier way

It's easy to connect Monkey Learn + Facebook Groups without coding knowledge. Start creating your own business flow.

    Triggers
  • New Event

    Triggers when a new event is created for a group.

  • New Photo

    Triggers when a new photo is added to a group's feed.

  • New Post

    Triggers when a new status is added to a group's feed.

  • New Video

    Triggers when a new video is added to a group's feed.

    Actions
  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

  • Post Message

    Creates a new message post in a group's feed.

  • Post Photo

    Creates a new photo post in a group's feed.

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Page reviewed by: Abhinav Girdhar  | Last Updated on July 01, 2022 5:55 am

How Monkey Learn & Facebook Groups Integrations Work

  1. Step 1: Choose Monkey Learn as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick Facebook Groups as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Monkey Learn to Facebook Groups.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Monkey Learn and Facebook Groups

  • Introduction (300 words)
  • Monkey Learn is an API that allows users to train machine learning models without writing any code. The API provides a large variety of pre-trained models for text, image recognition, sentiment analysis, and more. In this article, we will look at how to use Monkey Learn to create a Facebook group for your ML model training.

    Facebook groups are private communities, which can be created by a Facebook page or a user. A Facebook group has a discussion area where you can upload photos and videos. There is also a discussion board on the left side of the page where you can post information about your project or interest. There is a separate discussion area for each group, but you can also allow some groups to have a public URL. This way people can see the group’s discussions on Facebook search results.

    (1000 words)

    In this section, we will look into how to integrate Monkey Learn with Facebook groups. You will learn how to create a group from scratch, and then once you have a group, you will write a script to automatically create a Facebook group from a Token. Once you have a script to automatically create a Facebook group from a Token, you will be able to create multiple groups from one single account. You can then run your machine learning models on those groups using the API.

  • Integration of Monkey Learn and Facebook Groups (500 words)
  • In order to integrate Monkey Learn with Facebook groups, you need to do the fplowing:

    • Create a Facebook group from scratch
    • Write a script to automatically create Facebook groups from tokens
    • Create multiple Facebook groups from one single account
    • Run your ML models on those different groups using the API
    • Creating a Facebook Group from scratch (200 words)

    Creating a Facebook group is very easy and it only requires you to fplow the next steps:

    Go to https://www.facebook.com/groups/. Click on “Create Group” and type in the name of the group that you want to create. Click on “Create Group” and you now have your own Facebook group!

    • Writing a script to automatically create Facebook groups from Tokens (400 words)

    There is an excellent NLP library called pyTorch-NLP that we will use in our script. It comes with two classes. nlp_model_factory and nlp_model_factory_from_json . We will start by importing them:

    from torchvision import transforms from torchvision.transforms import register_transforms from pytorch_nlp import nlp_model_factory, nlp_model_factory_from_json from pytorch_nlp import tokenizer, word2vec import torch import json import re import os import csv import os.path import urllib.request import urllib.parse import threading import time import logging logger = logging.getLogger(__name__. class Tokenizer(object). def __init__(self). self._tokenize = [] self._tokenize = list(tokenizer.Tokenizer('./tokens/english-tokenization.txt'). def tokenize(self, text). return self._tokenize[0].tokenize(text. def tokenize_all(self, text). return list(self._tokenize[0].tokenize(text). def __repr__(self). s = '<Tokenizer {}>'.format(self. s += ', tokenize={}'.format(repr(self._tokenize). s += ', tokenize_all={}'.format(repr(self._tokenize_all). return s def __len__(self). return len(self._tokenize. def __getitem__(self, index). return self._tokenize[index] def __str__(self). return ' '.join([str(i. for i in self._tokenize]. class TokenizerParsingError(Exception). pass class TokenizerParsingFailed(Exception). pass class TokenizerStopIteration(Exception). pass class TokenizerIterator(object). def __init__(self). self._text = '' self._index = 0 self._modified = False self._saved = {} def __iter__(self). return self._iterator(. def next(self). if not self._modified. self._modified = True self._text += self._iterator(. self._index += 1 if not self._text. raise TokenizerStopIteration(. return str(self._text[-1]. def __next__(self). if not self._saved. self._save(. return self._next(. def _save(self). if not self._saved. self._modified = True self._saved = { 'index'. str(self._index), 'text'. str(self._text), } def _iterator(self). return iter([str(i. for i in self._text]. def _iterator_with_positionals(self). return iter([str(i), str(j. for i, j in zip([], [str(i), str(j), str('=')]). class EnglishTokenizer(). ""The class that implements the tokenizer"" def __init__(self). super(.__init__(. # parse our file on load parser = json.JSONParser(. parser.feed('English-Tokenization/english-tokenization.txt', self. # add our custom methods on top of the builtin ones for attrname in ('start', 'end'). setattr(self, attrname, getattr(parser, attrname). def tokenize(self, text). return [TokenizerParsingFailed(), TokenizerParserError(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerStopIteration()] def tokenize_all(self, text). return [TokenizerParsingFailed(), TokenizerParserError(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerIterator(), TokenizerStopIteration()] class EnglishTokenizerFactory(). ""The class that creates instances of the English tokenizer"" def __init__(self). super(.__init__(. # create a pre-initialized instance of our tokeniser english = EnglishTokenizer(. # add our custom methods on top of the builtin ones for attrname in ('stop', 'start'). setattr(english, attrname, getattr(english, attrname). # build our actual factory factory = nlp_model_factory_from_json.create_tokenizers('english', {}. factory['classifier'] = EnglishTokenizerFactory(. # register our tokenizers so that they can be used with transforms register_transforms('english', english. class FacebookGroupFactory(). ""The class that creates instances of the Facebook groups"" def __init__(self). super(.__init__(. # create a pre-initialized instance of our group initialisedFBGroup = facebook.FacebookGroup('your-group-name'. # add our custom methods on top of the builtin ones for attrname in ('stop', 'start'). setattr(initialisedFBGroup, attrname, getattr(initialisedFBGroup, attrname). # build our actual factory factory = nlp_model_factory_from_json.create_fbGroups('your-group-name', {}. factory['classifier'] = FacebookGroupFactory(. # register our groups so that they can be used with transforms register_transforms('your-group-name', initialisedFBGroup. class FacebookGroupFromTokenFactory(). ""The class that creates instances of the Facebook groups"" def __init__(self). super(.__init__(. # create a pre-initialized instance of our group initialisedFB

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