Personalized natural language processing system

A personalized natural language processing method, comprising:

• receiving or retrieve a plurality of sets of raw text data from a plurality of users, respectively;
• tokenizing the plurality of sets of raw text data to generate a plurality of sets of tokenized text data for the plurality of users, respectively, each set of tokenized text data including a sequence of tokens corresponding to the raw text data, the tokens at least identifying distinct words or portions of words in the raw text data;
• appending predetermined user-specific tokens to the plurality of sets of tokenized text data from the plurality of users, respectively, to generate a plurality of user-specific token sets, each predetermined user-specific token corresponding to one user of the plurality of users;
• processing the plurality of user-specific token sets sing a natural language processing (NLP) model in accordance with the appended predetermined user-specific tokens to predict a personalized classification for each of the plurality of user-specific token sets;
• and
• outputting the personalized classifications of the plurality of user-specific token sets, wherein the NLP model is trained using a training data set comprising multiple tuples of training data, each tuple including user identifier data that identifies a particular user, raw text data from the particular user, and ground truth classification data, the trained NLP model being configured to perform personalized text classification and/or text prediction tasks for each user of the multiple users, and during processing of the plurality of user-specific token sets, embeddings are produced for each token in each set of the plurality of user-specific token sets, and attention weights are computed between each token in each user-specific token set using the embeddings for each token.

A personalized natural language processing system comprising:

• at least one processor, communicatively coupled to non-volatile memory storing a sentiment analysis model and instructions that, when executed by the processor, cause the processor to:
• receive or retrieve a plurality of sets of utterances from a plurality of users, respectively;
• tokenize the plurality of sets of utterances to generate a plurality of sets of tokenized text data for the plurality of users, respectively, each set of tokenized text data including a sequence of tokens corresponding to the raw text data, the tokens at least identifying distinct words or portions of words in the raw text data;
• append predetermined user-specific tokens to the plurality of sets of tokenized text data from the plurality of users, respectively, to generate a plurality of user-specific token sets, each predetermined user-specific token corresponding to one user of the plurality of users;
• process the plurality of user-specific token sets using the sentiment analysis model in accordance with the appended predetermined user-specific tokens to predict a personalized classification for each of the plurality of user-specific token sets;
• and
• output the personalized classifications of the plurality of user-specific token sets, wherein the sentiment analysis model is trained using a training data set comprising multiple tuples of training data, each tuple including user identifier data that identifies a particular user, raw text data from the particular user, and ground truth classification data, the trained NLP model being configured to perform personalized text classification and/or text prediction tasks for each user of the multiple users, during processing of the plurality of user-specific token sets, embeddings are produced for each token in each set of the plurality of user-specific token sets, and attention weights are computed between each token in each user-specific token set using the embeddings for each token, and the personalized classifications include a plurality of sentiment labels including at least a positive sentiment, a neutral sentiment, and a negative sentiment.