Systems and methods for converting electronic messages from an externally shared communication channel in a group-based communication platform into conversation data

A computer-implemented method for converting electronic messages into conversation data, the method comprising:

• receiving, by one or more processors, and via an Application Programming Interface (API), electronic message data from an externally shared communication channel in a group-based communication platform, wherein the electronic message data comprises:
• a plurality of electronic messages;
• a respective user associated with each electronic message of the plurality of electronic messages;
• a respective channel or group associated with each electronic message;
• and
• a respective time or date associated with each electronic message;
• receiving, by one or more processors, electronic text message data from an instant electronic text messaging application separate from the externally shared communication channel in the group-based communication platform;
• generating, by the one or more processors, a database that represents the electronic message data and the electronic text message data on a database in a message per row format;
• generating conversation data by grouping, by the one or more processors, using a trained machine learning model, the electronic messages and electronic text messages in the database together into one or more conversations based on the electronic message data and electronic text message data, wherein the trained machine learning model has been trained based on (i) training electronic message data and electronic text message data that includes information regarding one or more electronic messages associated with the electronic message data and one or more electronic text messages associated with the electronic text message data and (ii) training conversation data that includes a prior category for each of the one or more electronic messages and the one or more electronic text messages, to learn relationships between the training electronic message data and text message data and the training conversation data, such that the trained machine learning model is configured to use the learned relationships to determine a conversation for an electronic message or electronic text message in response to input of data related to the electronic message or electronic text message;
• and
• outputting, by the one or more processors, the generated conversation data in a form of one or more of:
• a conversational HTML file;
• a text file;
• a CSV file associated with each user associated with each electronic message;
• a CSV file containing each electronic message and respective metadata associated with each electronic message;
• or
• a CSV file associated with each channel or group associated with each electronic message.

A system for converting electronic messages into conversation data, the system comprising:

• at least one memory storing instructions;
• and
• at least one processor executing the instructions to perform a process including:
• receiving, via an Application Programming Interface (API), electronic message data from an externally shared communication channel in a group-based communication platform, wherein the electronic message data comprises:
• a plurality of electronic messages;
• a respective user associated with each electronic message of the plurality of electronic messages;
• a respective channel or group associated with each electronic message;
• and
• a respective time or date associated with each electronic message;
• generating a database that represents the electronic message data in a message per row format;
• generating conversation data by grouping, using a trained machine learning model, the electronic messages in the database into one or more conversations based on the electronic message data, wherein the trained machine learning model is trained based on (i) training electronic message data that includes information regarding one or more electronic messages associated with the electronic message data and (ii) training conversation data that includes a prior category for each of the one or more electronic messages, to learn relationships between the training electronic message data and the training conversation data, such that the trained machine learning model is configured to use the learned relationships to determine a conversation for an electronic message in response to input of data related to the electronic message;
• and
• outputting the generated conversation data in a form of one or more of:
• a conversational HTML file;
• a text file;
• a CSV file associated with each user associated with each electronic message;
• a CSV file containing each electronic message and respective metadata associated with each electronic message;
• or
• a CSV file associated with each channel or group associated with each electronic message.