Social Customer Care

This application is a Starter Kit (SK) that is designed to get you up and running quickly with a common industry pattern, and to provide information and best practices around Watson services. This application was created to demonstrate how the Natural Language Classifier can be used to direct customer requests and queries to the appropriate agent or workflow. Additionally, Tone Analyzer, Alchemy Language, and Personality Insights demonstrate how to efficiently provide an agent with customer insights.

You can see a version of this app that is already running here.

IMPORTANT:

1. This application requires an AlchemyAPI key with high transaction limits. The free AlchemyAPI key that you request has a limit of 1000 transactions per day, which is insufficient for significant use of this sample application. See step 3 of the Getting Started section for information about requesting a higher transaction limit on your sample AlchemyAPI key.

2. Running the application will require twitter credentials to access the twitter stream. These credentials can be created at apps.twitter.com. These must be provided in the .env.js file.

3. The Natural Language Classifier requires training prior prior to running the application.

Table of Contents

• Getting Started
• Running the application locally
• About the Social Customer Care pattern
• Adapting/Extending the Starter Kit
• Troubleshooting

Getting Started

The application written in Node.js and uses npm. Instructions for downloading and installing these are included in the following procedure.

1. Log into GitHub and fork the project repository. Clone your fork to a folder on your local system and change to that folder.
2. Create a Bluemix Account. Sign up in Bluemix, or use an existing account. Watson Beta or Experimental Services are free to use.
3. If it is not already installed on your system, download and install the Cloud-foundry CLI tool.
4. If it is not already installed on your system, install Node.js. Installing Node.js will also install the npm command. Make sure to use node version 4.2.1 as specified in package.json or you may run into problems like installation issues.
5. Edit the manifest.yml file in the folder that contains your fork and replace application-name with a unique name for your copy of the application. The name that you specify determines the application's URL, such as application-name.mybluemix.net. The relevant portion of the manifest.yml file looks like the following:

declared-services:
  natural-language-classifier-service:
    label: natural_language_classifier
    plan: standard
  personality-insights-service:
    label: personality_insights
    plan: tiered
  tone-analyzer-standard:
    label: tone_analyzer
    plan: standard
applications:
  - services:
    - natural-language-classifier-service
    - personality-insights-service
    - tone-analyzer-standard
  name: social-customer-care
  command: npm start
  path: .
  memory: 512M

6. Connect to Blumix by running the following commands in a terminal window:

$ cf api https://api.ng.bluemix.net
$ cf login -u <your-Bluemix-ID> -p <your-Bluemix-password>

7. Create instances of the required Watson services.

• Create an instance of the Natural Language Classifier service by running the following command:

$ cf create-service natural_language_classifier standard natural-language-classifier-service

Note: You will see a message that states "Attention: The plan standard of service natural_language_classifier is not free. The instance classifier-service will incur a cost. Contact your administrator if you think this is in error.". The first Natural Language Classifier instance that you create is free under the standard plan, so there will be no change if you only create a single classifier instance for use by this application.

• If you already have an Alchemy Language service you will use those credentials. Othwerise, create an instance of the Alchemy Language service by running the following command:

$ cf create-service alchemy_api free alchemy-language-service

Note: The free plan of alchemy api has a limit of 1000 API calls a day. This is only sufficient to run quick tests of the starter kit but not fully support the application. The standard plan is not limited w

• Create an instance of the Personality Insights service by running the following command:

$ cf create-service personality_insights tiered personality-insights-service

Note: You will see a message that states "Attention: The plan tiered of service personality_insights is not free. The instance personality-insights-service will incur a cost. Contact your administrator if you think this is in error." The first 100 API calls each month are free, so if you remain under this limit there will be no charge.

• Create an instance of the Tone Analyzer service by running the following command:

$ cf create-service tone_analyzer standard tone-analyzer-service

Note: You will see a message that states "Attention: The plan standard of service tone_analyzer is not free. The instance tone-analyzer-service will incur a cost. Contact your administrator if you think this is in error." The first 1000 API calls each month are free, so if you remain under this limit there will be no charge.

8. Sign up at apps.twitter.com for application credentials. Create a new application with the Create new app button and fill out the required form.
9. Create and retrieve service keys to access the Natural Language Classifier

cf create-service-key natural-language-classifier-service myKey
cf service-key natural-language-classifier-service myKey

10. The Natural Language Classifier requires training prior to using the application. The training data is provided in data/classifier-training-data.csv. Adapt the following curl command to train your classifier (replace the username and password with the service credentials of the Natural Language Classifier created in the last step):

curl -u "{username}":"{password}" -F training_data=@data/classifier-training-data.csv -F training_metadata="{\"language\":\"en\",\"name\":\"My Classifier\"}" "https://gateway.watsonplatform.net/natural-language-classifier/api/v1/classifiers"

11. Create and retrieve service keys for the Alchemy Language service. If you are using an existing alchemy service, use those credentials instead.

cf create-service-key alchemy-language-service myKey
cf service-key alchemy-language-service myKey

12. Provide the credentials to the application by creating a .env.js file using this format:

module.exports = {
  TWITTER: JSON.stringify([{
    consumer_key: 'consumer_key',
    consumer_secret: 'consumer_secret',
    access_token_key: 'access_token_key',
    access_token_secret: 'access_token_secret'
  }]),
  TWITTER_TOPIC: '@support',
  CLASSIFIER_ID: ' CLASSIFIER ID',
  ALCHEMY_API_KEY: 'ALCHEMY KEY',
  DEBUG: 'scc:*'
};

13. Push the updated application live by running the following command:

$ cf push

Running locally

First, make sure that you followed steps 1 through 9 in the [previous section](#Getting Started) and that you are still logged in to Bluemix.

1. Expand the .env.js file created previously in the root directory of the project with the following content (the template is provided in the env.js file):

module.exports = {
  VCAP_SERVICES: JSON.stringify({
   natural_language_classifier: [{
     credentials: {
       url: 'https://gateway.watsonplatform.net/natural-language-classifier/api',
       username: 'USERNAME',
       password: 'PASSWORD'
     }
   }],
   tone_analyzer: [{
     credentials: {
       url: 'https://gateway.watsonplatform.net/tone-analyzer/api',
       username: 'USERNAME',
       password: 'PASSWORD'
     }
   }],
   personality_insights: [{
     credentials: {
       url: 'https://gateway.watsonplatform.net/personality-insights/api',
       username: 'USERNAME',
       password: 'PASSWORD'
     }
   }]
  }),
  TWITTER: JSON.stringify([{
    consumer_key: 'consumer_key',
    consumer_secret: 'consumer_secret',
    access_token_key: 'access_token_key',
    access_token_secret: 'access_token_secret'
  }]),
  TWITTER_TOPIC: '@support',
  CLASSIFIER_ID: ' CLASSIFIER ID',
  ALCHEMY_API_KEY: 'ALCHEMY KEY',
  DEBUG: 'scc:*'
};

2. Copy the username, password, apikey, and url credentials from your alchemy-language-service, classifier-service, tone-analyzer-service, and personality-insights-service services in Bluemix to the previous file. To see the service credentials for each of your service instance run the following command, replacing <application-name> with the name of the application that you specified in your manifest.yml file:

$ cf env <application-name>

3. Install any dependencies that a local version of your application requires:

$ npm install

4. Install Gulp globally if not already installed:

$ npm install -g gulp

5. Start the application by running:

$ gulp

6. Open http://localhost:5000 to see the running application.

About the Social Customer Care pattern

For a given input, a trained Natural Language Classifier responds with a list of intent classes and a confidence score. A frequent use case is to use the user intent and confidence scores to determine how best to assist a customer. This pattern can be used to send customers to an appropriate agent, to respond to commonly asked questions, and to begin the appropriate dialog conversation as is done with the Conversational Agent Starter Kit. This Starter Kit demonstrates the basic approach of how the Natural Language Classifier can be easily used to improve customer support.

To demonstrate this approach, the Starter Kit uses a live stream of the tweets being sent to Twitter's @Support account. These tweets are sent to a trained Natural Language Classifier. The resulting intents and confidence scores are used to delegate the tweets or provide a potential response.

The results of Tone Analyzer, Alchemy Language, and Personality Insights services are used to provide quick insight into the customer. Tone Analyzer is used to determine the current mood of the user based on their tweet. Alchemy Language and Personality Insights are ran on each the customer's past tweets. The entity and keyword extraction of Alchemy Language provides a quick snapshot of the topics that the customer typically tweets about and Personality Insights determines provides an estimate of the customer's personality. This information can be used to provide an agent with quick insight into their customer or even to help send a customer to the most appropriate agent (ex. avoid sending irate customers to a new agent).

Note: For the purposes of this starter kit, random bodies of text are substituted for the customer's past tweets. This is done to avoid the issues involved with retrieving past at scale. For your application, past tweets can be easily retrieved by using the IBM Insights for Twitter Service available on Bluemix.

Adapting/Extending the Starter Kit

This Starter Kit works off of Twitter data. However, the concepts used here are platform independent and can be applied wherever you already do customer support. This includes email, sms, Facebook, and chat/messaging apps.

The following are a basic set of instructions for how to adapt the Starter Kit to your own use case.

1. The Twitter feed can easily be changed by modifying the TWITTER_TOPIC variable in your .env.js file.
2. The Natural Language Classifier Service needs a new ground truth for your new feed. The best approach is to collect real user tweets. An easy solution is to use the Insights for Twitter service available on Bluemix. Using this service, you can retrieve historical tweets from the Twitter Decahose (a 10% random sample of Tweets). Another alternative is to modify the starter kit to save incoming tweets and run the application locally. The number of tweets required will depend upon the complexity of the feed. In most cases, around 300 tweets is enough to receive respectable performance for a proof of concept, demo, or testing.
3. Create the ground truth for the Natural Language Classifier by classifying the tweets that you collected in the previous step. Further details on this process can be found in the link below.
4. Decide how the application will handle each use case. The approach taken by the Starter Kit is to provide a faq style response or to delegate the customer to an appropriate agent. This can be customized by altering the data/default-responses.json file. Be sure to include an entry for each intent in your classifier ground truth.
5. Use the personality insights profile to drive your customer engagement strategy. The Starter Kit simply displays a few highlights from the customer's personality but much more is possible. Some examples of how to apply the service can be found in the Personality Insight documentation.

Reference information

The following links provide more information about the Natural Language Classifier, Tone Analyzer, Alchemy Language, and Personality Insights services.

Natural Language Classifier

• API documentation: Get an in-depth knowledge of the Natural Language Classifier service
• API reference: SDK code examples and reference
• API Explorer: Try out the API
• Creating your own classifier: How to use the API to create and use your own classifier

Tone Analyzer

• API documentation: Get an in-depth knowledge of the Natural Language Classifier service
• API reference: SDK code examples and reference
• API explorer: Try out the REST API

AlchemyAPI

• API documentation: Get an in-depth understanding of the AlchemyAPI services
• AlchemyData News reference: API and query gallery

Personality Insights

• API documentation: Get an in-depth understanding of the Personality Insights services
• API reference: SDK code examples and reference
• API explorerer: Try out the REST API

Troubleshooting

To troubleshoot your Bluemix application, use the logs. To see the logs, run:

$ cf logs <application-name> --recent

License

This sample code is licensed under Apache 2.0. Full license text is available in LICENSE.

Contributing

See CONTRIBUTING.

Open Source @ IBM

Find more open source projects on the IBM Github Page

Privacy Notice

This node sample web application includes code to track deployments to Bluemix and other Cloud Foundry platforms. The following information is sent to a Deployment Tracker service on each deployment:

• Application Name (application_name)
• Space ID (space_id)
• Application Version (application_version)
• Application URIs (application_uris)

This data is collected from the VCAP_APPLICATION environment variable in IBM Bluemix and other Cloud Foundry platforms. This data is used by IBM to track metrics around deployments of sample applications to IBM Bluemix. Only deployments of sample applications that include code to ping the Deployment Tracker service will be tracked.

Disabling Deployment Tracking

Deployment tracking can be disabled by removing require('cf-deployment-tracker-client').track(); from the beginning of the server.js file at the root of this repo.