Method and system for evaluating contract worthiness of performing artists

A system for evaluating contract worthiness of performing artists, the system comprising:

• a processor;
• and
• a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to:
• receive consolidated data corresponding to each of a set of performing artists from one or more data sources, wherein the consolidated data comprise text data and a plurality of metrics from a plurality of digital platforms;
• process the consolidated data, wherein to process the consolidated data, the processor is configured to:
• classify a set of topics in the text data into a plurality of categories through a Machine Learning (ML) classification model:
• determine, for at least one of the plurality of categories, one or more user engagement behavior segments based on sentiment scores associated with the text data, wherein the sentiment scores is determined using an ML sentiment analysis model;
• and
• determining, by the evaluation device, a plurality Key Performance Indicators (KPIs) based on the plurality of metrics, the plurality of categories, and the one or more user engagement behavior segments:
• calculate a contract worthiness score for each of the set of performing artists based on the plurality of KPIs using a trained ML contract worthiness scoring model;
• and
• evaluate one or more of the set of performing artists for their contract worthiness based on the contract worthiness score and a threshold contract worthiness score, wherein the threshold contract worthiness score is determined based on a historical distribution of contract worthiness scores for the trained ML contract worthiness scoring model.

A non-transitory computer-readable medium storing computer-executable instructions for evaluating contract worthiness of performing artists, the computer-executable instructions configured for:

• receiving consolidated data corresponding to each of a set of performing artists from one or more data sources, wherein the consolidated data comprise text data and a plurality of metrics from a plurality of digital platforms;
• processing the consolidated data by:
• classifying a set of topics in the text data into a plurality of categories through a Machine Learning (ML) classification model;
• determining, for at least one of the plurality of categories, one or more user engagement behavior segments based on sentiment scores associated with the text data, wherein the sentiment scores is determined using an ML sentiment analysis model;
• and
• determining a plurality Key Performance Indicators (KPIs) based on the plurality of metrics, the plurality of categories, and the one or more user engagement behavior segments:
• calculating a contract worthiness score for each of the set of performing artists based on the plurality of KPIs using a trained ML contract worthiness scoring model;
• and
• evaluating one or more of the set of performing artists for their contract worthiness based on the contract worthiness score and a threshold contract worthiness score, wherein the threshold contract worthiness score is determined based on a historical distribution of contract worthiness scores for the trained ML contract worthiness scoring model.