Method of system of providing local personalized emoticon recommendation

A method for providing local emoticon recommendations by a client device, the method comprising:

• receiving input data at an application including a local emoticon recommendation engine on the client device of a user that offers use of emoticons in communications between the user and one or more other users;
• pre-processing the input data via the local emoticon recommendation engine by removing at least one of stop words, punctuation, and special characters, to generate one or more text segments for which an emoticon will be used from the input data;
• receiving an indication of a likelihood for the user to utilize the emoticon in a communication between the user and the one or more other users, the indication being received via a user interface screen of an application installed on the client device;
• retrieving contextual data associated with the one or more text segments;
• following the pre-processing, tokenizing the one or more text segments and the contextual data via the local emoticon recommendation engine to generate one or more tokenized words from the one or more text segments and the contextual data;
• tagging the tokenized words via the local emoticon recommendation engine by querying a natural language library to generate a part-of-speech (POS) tag set for each of the one or more tokenized words;
• providing the generated POS tag set to a local machine learning (ML) model trained for providing emoticon recommendations based on the POS tag set;
• executing the local ML model on the client device to provide one or more emoticon recommendations;
• displaying the one or more emoticon recommendations to the user as user selectable recommendations;
• and
• implementing ongoing training to the local ML model, wherein training the local ML model includes associating each POS tag set in a list of POS tag sets with ranked emoticons.

A non-transitory computer readable medium on which are stored instructions that, when executed, cause a programmable device to perform functions of:

• receiving input data at an application including a local emoticon recommendation engine on a client device of a user that offers use of emoticons in communications between the user and other users;
• pre-processing the input data via the local emoticon recommendation engine by removing at least one of stop words, punctuation, and special characters, to generate one or more text segments from the input data;
• following the pre-processing, tokenizing the one or more text segments via the local emoticon recommendation engine to generate one or more tokenized words from the one or more text segments, the one or more text segments being received as part of an input data received by an application that offers use of emoticons in communications between users;
• tagging the tokenized words via the local emoticon recommendation engine by querying a natural language library to generate a part-of-speech (POS) tag set for each of the one or more tokenized words;
• providing the generated POS tag set to a local machine learning (ML) model trained for providing emoticon recommendations based on the POS tag set and a library of converted emoticons;
• executing the local ML model on the programmable device to provide one or more emoticon recommendations;
• displaying the one or more emoticon recommendations to the user as user selectable recommendations;
• and
• providing ongoing training to the local ML model, wherein training the local ML model includes associating each POS tag set in a list of POS tag sets with ranked emoticons.