Abstract: Techniques for generating graphical elements via a communication platform are discussed herein. For example, one or more machine-learning models associated with a communication platform may be configured to receive, as input and from a user of the communication platform, a sentiment and/or a graphical element. The machine-learning model may be trained, using prior natural language statements and prior confidence levels associated with previous graphical elements, to output one or more graphical elements associated with the input. The one or more graphical elements may be shared via the communication platform and used to accurately and effectively convey thoughts, emotions, reactions, and ideas, for example.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. In a group-based communication system, a user may save posts for later (e.g., to reply to a message at a later time, to complete a task associated with a message at a later time). The system may use a machine learning model to determine to automatically mark a post for later for a user, for example, based on a set of features including at least a semantic embedding of the post. Additionally, or alternatively, the system may use a machine learning model to determine an order for displaying items (e.g., posts, reminders, files) within a user view (e.g., a later tab, a drafts tab, a threads tab, a files tab) for a user via a user interface. The system may update one or more machine learning models based on how users interact with the posts, user views, or both.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A group-based communication system may use machine learning techniques to classify teams of the system, determine discount messaging for teams of the system, or both. The group-based communication system may receive concurrency data for a team of users and may input the concurrency data (e.g., with one or more other features associated with the team) into a machine learning model to generate a classifier for the team. The classifier may indicate whether the team is a work team, an educational team, or a social team. Based on the classifier for the team, the system may send a message to at least one user of the team (e.g., an administrative user). In some examples, the system may use another machine learning model to generate a discount message for sending to the at least one user based on the team classifier.

Abstract: Techniques for generating user profile data including one or more frequent channels, related users, and/or related topics within a communication platform are discussed herein. In some examples, a machine-learning model can receive user interaction data (messages sent, messages read, channel posts, documents shared, frequent key words used, etc.) associated with the communication platform and output one or more frequent channels, related users, and/or related topics. The communication platform may then associate the one or more frequent channels, related users, and/or related topics with the user's profile data. In some examples, the communication platform may present different frequent channels, related users, and/or related topics associated with a profile page based on interaction action associated with the user account viewing the profile page.

Abstract: Techniques for generating graphical elements via a communication platform are discussed herein. For example, one or more machine-learning models associated with a communication platform may be configured to receive, as input and from a user of the communication platform, a sentiment and/or a graphical element. The machine-learning model may be trained, using prior natural language statements and prior confidence levels associated with previous graphical elements, to output one or more graphical elements associated with the input. The one or more graphical elements may be shared via the communication platform and used to accurately and effectively convey thoughts, emotions, reactions, and ideas, for example.

Abstract: In association with a communication platform, a machine learning component may determine affordances to provide to users, where affordances describe features provided by the communication platform. The machine learning component is trained using log data representing interactions and features used (or not used) by users. In some examples, the log data is associated with members of one or more groups while, in other examples, the log data is associated with all of the users of the communication platform. To determine an affordance, the machine learning component analyzes an interaction between a user and the communication platform. Based on the analysis, the machine learning component determines a relationship between the interaction and a feature. The machine learning component then generates the affordance to include information about the feature. Additionally, a user interface then provides the affordance to the user, such as in proximity to the feature.

Abstract: Systems, methods, and computer-readable media are provided for adding connections for a user. External graphs may be analyzed to identify connections and relationships which may be missing in a group-based communication system. These missing connections may be suggested to the user as a suggested virtual space. A user may then create the virtual space based on the suggestions.