Getting started
Note:
Currently, this functionality is available for open beta testing.
Enabling Smart Conversations is a very straightforward process. The steps required vary slightly based on the functionalities you select.
Prerequisites
The Smart Conversations Functionality is an add-on feature of the Conversation API. You must create and configure at least one Conversation API app with one channel in order to use Smart Conversations. To get started with the Conversation API, follow this guide.
Getting started with Smart Conversations
After setting up a Conversation API app and configuring at least one channel on that app, you can enable specific Smart Conversations features from the Smart Conversations section of your app's configuration page on the Sinch Customer Dashboard.In addition, depending on which functionalities you enabled, you may need to train the Smart Conversations Machine Learning engine(s) on data sets in order to ensure accurate results. The table below lists the functionalities that are currently available, provides a brief description, and includes information on how to prepare training data sets.
| Feature | Description | Description | Information and sample requirements | More information |
|---|---|---|---|---|
| Sentiment Analysis | Provides an assessment of the likelihood that the emotional tone of a message is positive, negative, or neutral. | Provides an assessment of the likelihood that the emotional tone of a message is positive, negative, or neutral. For information on how this analysis is represented in a callback, see the description of the ml_sentiment_result array. | There is no minimum sample set requirement. | |
| Natural Language Understanding (NLU) | Provides an assessment of the likelihood that the message corresponds with a specific set of intents. For example, the likelihood that a message is a greeting, a request for information, or an expression of satisfaction or dissatisfaction. | Provides an assessment of the likelihood that the message corresponds with a specific set of intents. For example, the likelihood that a message is a greeting, a request for information, or an expression of satisfaction or dissatisfaction. For information on how this analysis is represented in a callback, see the description of the ml_nlu_result array. | There is no minimum sample set requirement. However, we recommended you have at least 50 samples of expressions for each intent to achieve accurate results. | More contextual information regarding Natural Language Understanding can be found on our Community site. |
| Image Comprehension Engine | Provides an analysis of images included in the received message. This includes the identification of probable document types in the image, optical character extraction, and the assignment of values to probable fields identified on the image. | Provides an analysis of images included in the received message. This includes the identification of probable document types in the image, optical character extraction, and the assignment of values to probable fields identified on the image. For information on how this analysis is represented in a callback, see the description of the ml_image_recognition_result array | For Document Image Classification (DIC), a data set of at least 100 images for each document class is required. For Document Field Classification (DFC), the regex patterns for each field to be extracted must be provided. For more information, click here. | More contextual information regarding the Image Comprehension Engine, which is also called IRIS, can be found on our Community site. |
| PII Masking | Provides an analysis of content included in the received message to identify and mask sections of text that correspond to any representation of information that discloses the identity of an individual. Name, phone number, national ID, email, and other sensitive pieces of information are considered Personal Identifiable Information (PII) and will be masked. | Provides an analysis of content included in the received message to identify and mask sections of text that correspond to any representation of information that discloses the identity of an individual. Name, phone number, national ID, email, and other sensitive pieces of information are considered Personal Identifiable Information (PII) and will be masked. For information on how this analysis is represented in a callback, see the description of the ml_pii_result array | There is no minimum sample set requirement. By default, the following common PII are supported/recognized. You can enable/disable each one to configure how PII should be identified during redaction. Recognized PII:
| |
| Offensive Content Analysis | Provides an assessment of the likelihood that the analyzed text or image message contains offensive content (for example, explicit images, hate speech, offensive language, etc.). | Provides an assessment of the likelihood that the analyzed text or image message contains offensive content (for example, explicit images, hate speech, offensive language, etc.). For information on how this analysis is represented in a callback, see the description of the ml_offensive_analysis_result array | There is no minimum sample set requirement. |
Now that you have configured Smart Conversations and trained on test data, you can begin using the Smart Conversations callbacks.
