We Claim:
1. A method (2000) for evaluating contract worthiness of performing artists, the method (2000)
comprising:
receiving (2002), by an evaluation device (102), consolidated data (206b) corresponding to each of a set of performing artists from one or more data sources, wherein the consolidated data (206b) comprise text data and a plurality of metrics from a plurality of digital platforms;
processing (2004), by the evaluation device (102), the consolidated data (206b) by: classifying (2004a), by the evaluation device (102), a set of topics (608) in the text data into a plurality of categories through a Machine Learning (ML) classification model;
determining (2004b), by the evaluation device (102) for at least one of the plurality of categories, one or more user engagement behavior segments based on sentiment scores (1024) associated with the text data, wherein the sentiment scores (1024) is determined using an ML sentiment analysis model; and
determining (2004c), by the evaluation device (102), 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 (2006), by the evaluation device (102), a contract worthiness score (1602)
for each of the set of performing artists based on the plurality of KPIs using a trained ML
contract worthiness scoring model; and
evaluating (2008), by the evaluation device (102), one or more of the set of performing artists for their contract worthiness based on the contract worthiness score (1602) and a threshold contract worthiness score, wherein the threshold contract worthiness score is determined based on a historical distribution of contract worthiness scores (1602) for the trained ML contract worthiness scoring model.
2. The method (2000) as claimed in claim 1, further comprising:
assigning (2010) a rank to each of the one or more of the set of performing artists based on the corresponding contract worthiness score; and
visualizing (2012) performing artist details and a plurality of charts corresponding to each of the one or more of the set of performing artists via a Graphical User Interface (GUI) (1700), wherein the performing artist details comprise the contract worthiness score, the

assigned rank, and a geographical location, wherein the plurality of charts is based on the plurality of KPIs, the plurality of social media metrics, the one or more user engagement behavior segments, and the plurality of categories, and wherein the plurality of KPIs comprises at least one of performing artist social presence related-KPIs (418), performing artist talent quality and persona-related KPIs (420), social media user-related KPIs (422), and future commercial viability-related KPIs (424).
3. The method (2000) as claimed in claim 1, further comprising assigning a label to each of the
set of performing artists based on the corresponding contract worthiness score and the threshold
contract worthiness score, wherein the label corresponds to one of relevant or non-relevant with
respect to the contract offering.
4. The method (2000) as claimed in claim 1, wherein the plurality of digital platforms
comprises a plurality of websites (202a) and a plurality of social media platforms (202b), and
wherein receiving the consolidate data (206b) further comprises:
extracting web text data corresponding to a plurality of performing artists, in a user-defined art domain, from at least one of the plurality of websites (202a);
applying a predefined filtering criteria on the web text data to identify the set of performing artists from the plurality of performing artists;
extracting the web text data and a plurality of web metrics corresponding to each of the set of performing artists from the plurality of websites (202a);
extracting social media text data and a plurality of social media metrics corresponding to each of the set of performing artists from the plurality of social media platforms (202b); and
for each of the set of performing artists, consolidating the web text data, the social media text data, the plurality of web metrics, and the plurality of social media metrics to obtain the consolidated data (206b).
5. The method (2000) as claimed in claim 1, wherein processing the consolidated data (206b)
comprises:
identifying a plurality of keywords in the consolidated data (206b) using one or more natural language processing models; and
training the ML classification model using the plurality of keywords.

6. The method (2000) as claimed in claim 1, wherein the text data comprises user comment and multimedia content description, wherein the plurality of categories comprises a plurality of user comment categorization and a plurality of multimedia content description categorization, and wherein classifying the set of topics (608) into the plurality of categories comprises segregating the text data using a plurality of rules, and wherein at least one of the plurality of rules is based on a social media account ownership.
7. The method (2000) as claimed in claim 1, further comprising generating the trained ML contract worthiness scoring model by:
creating a training dataset and a test dataset from historical data comprising the consolidated data (206b) for a plurality of artists in a pre-defined art domain with their known contract worthiness;
training each of a set of ML contract worthiness scoring models using the training dataset; and
selecting the trained ML contract worthiness scoring model based on a performance score of each of the set of ML contract worthiness scoring models with respect to the test dataset.
8. A system (100) for evaluating contract worthiness of performing artists, the system (100)
comprising:
a processor (106); and
a memory (104) communicatively coupled to the processor (106), wherein the memory (104) stores processor-executable instructions, which, on execution, causes the processor (106) to:
receive consolidated data (206b) corresponding to each of a set of performing artists from one or more data sources, wherein the consolidated data (206b) comprise text data and a plurality of metrics from a plurality of digital platforms;
process the consolidated data (206b), wherein to process the consolidated data (206b), the processor (106) is configured to:
classify a set of topics (608) 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 (1024)

associated with the text data, wherein the sentiment scores (1024) is
determined using an ML sentiment analysis model; and
determine 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 (1602) 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 (1602) and a threshold contract worthiness score, wherein the threshold contract worthiness score is determined based on a historical distribution of contract worthiness scores (1602) for the trained ML contract worthiness scoring model.
9. The system (100) as claimed in claim 8, wherein the processor (106) is further configured
to:
assign a rank to each of the one or more of the set of performing artists based on the corresponding contract worthiness score; and
visualize performing artist details and a plurality of charts corresponding to each of the one or more of the set of performing artists via a Graphical User Interface (GUI) (1700), wherein the performing artist details comprise the contract worthiness score, the assigned rank, and a geographical location, wherein the plurality of charts is based on the plurality of KPIs, the plurality of social media metrics, the one or more user engagement behavior segments, and the plurality of categories, and wherein the plurality of KPIs comprises at least one of performing artist social presence related-KPIs (418), performing artist talent quality and persona-related KPIs (420), social media user-related KPIs (422), and future commercial viability-related KPIs (424).
10. The system (100) as claimed in claim 8, wherein the plurality of digital platforms comprises
a plurality of websites (202a) and a plurality of social media platforms (202b), and wherein to
receive the consolidate data, the processor (106) is further configured to:
extract web text data corresponding to a plurality of performing artists, in a user-defined art domain, from at least one of the plurality of websites (202a);

apply a predefined filtering criteria on the web text data to identify the set of performing artists from the plurality of performing artists;
extract the web text data and a plurality of web metrics corresponding to each of the set of performing artists from the plurality of websites (202a);
extract social media text data and a plurality of social media metrics corresponding to each of the set of performing artists from the plurality of social media platforms (202b); and
for each of the set of performing artists, consolidate the web text data, the social media text data, the plurality of web metrics, and the plurality of social media metrics to obtain the consolidated data (206b).