Classification of user sentiment based on machine learning

A method for machine learning classification of user sentiment, the method comprising:

• obtaining a plurality of category information including a set of domain-specific category information based on text data;
• determining a plurality of clusters from the text data, each cluster of the plurality of clusters comprising one or more words from the text data;
• extracting a plurality of aspects from the plurality of clusters, the extracting comprising:
• determining a most frequent word as an explicit aspect for the one or more words of a cluster,
• determining a part of speech associated with the one or more words of the cluster,
• responsive to the part of speech, determining a set of synonyms of the one or more words of the cluster associated with an adjective part of speech,
• responsive to the adjective part of speech, generating lemmatized noun forms of the set of synonyms of the cluster,
• determining a match of the lemmatized noun forms with the explicit aspect of the cluster, and
• selecting the explicit aspect based on the match having a greatest number of occurrences to represent an aspect of an implicit cluster;
• and
• generating, by a machine learning model, a sentiment based on the plurality of aspects and the set of domain-specific category information,
• wherein the plurality of aspects comprises implicit aspects and explicit aspects of the plurality of clusters.

A method for gated machine learning of user sentiment classification, the method comprising:

• obtaining a plurality of category information including a set of domain-specific category information based on text data;
• determining a plurality of clusters from the text data, each cluster of the plurality of clusters comprising one or more words from the text data;
• determining a most frequent word as an explicit aspect for the one or more words of a cluster;
• determining a part of speech associated with the one or more words of the cluster;
• responsive to the part of speech, determining a set of synonyms of the one or more words of the cluster associated with an adjective part of speech;
• responsive to the adjective part of speech, generating lemmatized noun forms of the set of synonyms of the cluster;
• determining a match of the lemmatized noun forms with the explicit aspect of the cluster;
• selecting the explicit aspect based on the match having a greatest number of occurrences to represent an aspect of an implicit cluster;
• extracting a plurality of aspects corresponding to implicit aspects and explicit aspects from the plurality of clusters;
• filtering, by a gate mechanism, the plurality of category information based on a weight applied to the plurality of category information;
• passing, by the gate mechanism, a portion of the plurality of category information corresponding to the set of domain-specific category information based on the applied weight;
• and
• generating, by a machine learning model, a sentiment based on the plurality of aspects and the set of domain-specific category information.