Smart Conversations Overview
Smart Conversations is a collection of add-on features for Conversation API that passes received messages to the Sinch proprietary Machine Learning (ML) API engine. Each of these features collects and analyzes information present in the message. These results are then delivered via the SMART_CONVERSATION webhook.
Through this webhook, Smart Conversations delivers vital information that can help you automate processes, respond to feedback, simplify data entry, and solve other problems to help streamline your business operations.
The features that are currently available, along with a description of each, are listed below:
| Feature | Description | Description | Training data 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. |
Click here to get started with Smart Conversations.
