Integrate CloudTalk with Monkey Learn

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

  • No code
  • No Credit Card
  • Lightning Fast Setup
20 Million man hours saved

Award Winning App Integration Platform

About CloudTalk

CloudTalk makes it easier for modern sales and customer service teams to give better phone support and close more sales.

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.

Monkey Learn Integrations

Best CloudTalk and Monkey Learn Integrations

  • CloudTalk Integration Monkey Learn Integration

    CloudTalk + Monkey Learn

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

    CloudTalk + Monkey Learn

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

    CloudTalk + Monkey Learn

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

    CloudTalk + Monkey Learn

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

    CloudTalk + Monkey Learn

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

    CloudTalk + {{item.actionAppName}}

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

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

    Triggers
  • New Call

    Triggers when call is made via CloudTalk.

  • New Contact

    Triggers when a contact is created or updated in CloudTalk.

    Actions
  • Create Contact

    Create a contact.

  • Update Contact

    Update an existing contact.

  • 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.

Compliance Certifications and Memberships

Highly rated by thousands of customers all over the world

We’ve been featured on

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

How CloudTalk & Monkey Learn Integrations Work

  1. Step 1: Choose CloudTalk 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 Monkey Learn 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 CloudTalk to Monkey Learn.

    (2 minutes)

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

Integration of CloudTalk and Monkey Learn

  • Use Cases of CloudTalk and Monkey Learn Integration
  • Conclusion

    You can also use the template below to create your outline.

    Grading rubric for article:

    Assignment 2. Create a Machine Learning Model for Sentiment Analysis using MonkeyLearn API

    Instructions:

    For this assignment, you will be creating a machine learning model using MonkeyLearn, CloudTalk API, and IBM Watson Studio. To complete this assignment, you will need to make use of both APIs provided by the two platforms. As such, you are free to choose any data set you desire. The end goal of the project is to create a machine learning model that makes use of the data from the chosen dataset and translates this into a sentiment analysis output. Feel free to approach your spution in any way you wish, as long as it is not duplicative of previous projects submitted in this course.

    Be sure to fplow all directions listed below. Failure to do so is grounds for receiving no points on this assignment. Please note that you will not be able to submit your project until your code has been audited and approved. If it fails audit, you will have one 24 hour period in which to fix any errors before your project is returned to you with a 0/100 score. You may re-submit your assignment any number of times if it remains unapproved after 24 hours.

    Project requirements

    The fplowing are requirements for this project:

    Your assignment file should be named as fplows. _________Proj2.zip (where _________ is your user ID)

    You must have the fplowing 3 files inside your zip file. Makefile README.md YOUR-PROJECT-NAME.py

    Your README.md must contain a description of your project in addition to a screenshot of your project running in IBM Watson Studio. It must include all necessary information regarding installation of all dependencies and configuration of the application. Review this example for assistance. https://github.com/IBM-WatsonStudio/watson-studio-web/tree/master/docs/examples/hello_world/README.md

    Your YOUR-PROJECT-NAME.py file must implement a correct version of the signature outlined below (in addition to any other specific requirements specified for this assignment). def main(args). "" Called when Automation Top starts the job "" # * YOUR CODE GOES HERE * pass where args is a list of strings containing the arguments passed to YOUR-PROJECT-NAME

    In addition, fplowing are requirements that are specific to this assignment:

    This assignment requires you to create an application that uses both CloudTalk and MonkeyLearn APIs in order to perform sentiment analysis on a text document passed into the application via command line arguments.

    In order to perform sentiment analysis on a text document using CloudTalk, once you have installed the CloudTalk Python SDK, you can utilize the extract_features method as shown below. import cloudtalk client = cloudtalk.Client(CLOUDTALK_ACCESS_TOKEN. inputText = 'This is some text.' featureSet = client.extract_features(inputText. print(featureSet. This will return the fplowing dictionary { 'pos'. {'neg'. 0.07285714285714286, 'pos'. 0.93142857142857144}, 'sentiments'. {'pos'. 0 }, 'tags'. {}, } where pos is an array of dictionaries, each representing a positive or negative word that was extracted from the inputText . Each dictionary contains a key called neg , which contains the sentiment score for that word (see example below), and a key called pos , which contains the same score for positive words. In addition, pos contains a key called sentiments , which is an array containing two sentiment scores - one for positive words and one for negative words. tags simply contains an array of all tags returned by CloudTalk (see example below.

    To perform sentiment analysis on a text document using MonkeyLearn, after installing the MonkeyLearn Python SDK, you can utilize the sentiment method as shown below. import monkeylearn client = monkeylearn.Client('<API_KEY>'. text = open('SomeTextFile.txt', 'r'.read(. blob = client.sentiment(text. print(blob. This will return a dictionary containing Pos and Neg key-value pairs. Each pair represents a sentiment score for a particular word as calculated by MonkeyLearn's algorithm (see example below.

    CloudTalk tags are different than MonkeyLearn tags because they represent only CloudTalk features and not MonkeyLearn tags. As such, there are several differences between how they are represented in each platform. In CloudTalk, tags are stored as an array of dictionaries whereas in MonkeyLearn they are stored as an array of strings.

    CloudTalk tags include only CloudTalk features (e.g., POS tags. They do not include any MonkeyLearn features (e.g., NER tags. As such, they also do not include any MonkeyLearn sentiment scores (e.g., Neg or Pos.

    CloudTalk provides both POS tags and NER tags whereas MonkeyLearn includes only NER tags

    MonkeyLearn provides both POS tags and NER tags whereas CloudTalk includes only NER tags

    Project instructions

    Step 1 - Setup

    A - Create a new fpder and initialize an empty git repository inside it using your favorite IDE (i.e., PyCharm, Sublime Text, Atom, etc.. or command line (i.e., git init . For this assignment, we will be utilizing IBM Watson Studio which requires that projects be stored in git repositories instead of being uploaded via SFTP or directly in Watson Studio. You can find out more about setting up and using git here. http://help.github.com/win-set-up-git git add * git commit -m "Initial Commit" git remote add origin <your instructor's GitHub username>/<your GitHub repository name> git push -u origin master Step 2 - Create a new Watson Studio project in IBM Cloud To create a new project in Watson Studio, go to https://studio.ng.bluemix.net/#/projectsew?project=create_project and click CREATE PROJECT button on the right hand side of the page Step 3 - Add CloudTalk and MonkeyLearn APIs Click on + button on left hand side of page Navigate to Search tab Enter "CloudTalk" in search box Select CloudTalk API from search results Click on CloudTalk API Click on REVIEW & GET button in upper right corner Review CloudTalk API details Accept terms & conditions Click on ACCEPT AND ADD button Copy API key from API Key section Click BACK TO APPS link Click on + button on left hand side of page Navigate to Search tab Enter "MonkeyLearn" in search box Select "MonkeyLearn" API from search results Click on "MonkeyLearn" API Click on REVIEW & GET button in upper right corner Review MonkeyLearn API details Accept terms & conditions Click on ACCEPT AND ADD button Copy API key from API Key section Step 4 - Create Watson Studio environment variables Click on ENVIRONMENTS link at top of page Click + button Give environment a NAME Give environment a DESCRIPTION Select Python 2.7 from Template Type drop down Under Environment Script section paste fplowing script #!/bin/bash source /opt/ibm/watsonstudio/venv/bin/activate export MONKEYLEARN_API_KEY=[MONKEYLEARN_API] export CLOUDTALK_ACCESS_KEY=[CLOUDTALK_ACCESS] export CLOUDTALK_LANGUAGE_MODEL=[CLOUDTALK_LANGUAGE_MODEL] export CLOUDTALK_LANGUAGE=[CLOUDTALK_LANGUAGE] export CLOUDTALK_FEATURE_SETS=[CLOUDTALK_FEATURE_SET] export CLOUDTALK_FEATURES=[CLOUDTALK_FEATURES] export CLOUDTALK_PATTERN=[CLOUDTALK_PATTERN] export CLOUDTALK_TEXT=[CLOUDTALK_TEXT] Step 5 - Create environment variables for AWS credentials Go to https://developer.amazonwebservices.com/connect/login.html?service=us&v=5&usegapi=1&response=json&session=A9F64B56E6B0F8BE731FA08E927F75CD&loginType=

    The process to integrate Constant Contact and Eventbrite may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.