We Claim:
1. A method for identifying product-label from data on one or more social
media platforms, the method comprising:
- receiving, by a pre-trained processing unit [102], a set of data comprising one or more data points from the one or more social media platforms;
- predicting, by the pre-trained processing unit [102], one or more product-labels in the set of data;
- performing, by the pre-trained processing unit [102], an aggregation action, a bucketing action, and a filtering action on the predicted one or more product-labels;
- identifying, by the pre-trained processing unit [102], a set of target product-labels based on the aggregation action, the bucketing action, and the filtering action; and
- displaying, by a user interface [110], the set of target product-labels from the data on one or more social media platforms.

2. The method as claimed in claim 1, wherein the identified set of target product-labels is sent to a search engine unit for generating, by the search engine unit, a decision regarding visibility of the set of target product-labels on the digital platform, wherein the decision regarding visibility is one of automatically increasing the visibility, automatically decreasing the visibility, and no change in the visibility of the set of target product-labels on the digital platform.
3. The method as claimed in claim 1, wherein the pre-trained processing unit [102] is trained based on an annotated social media dataset, a title dataset, an sentiment-analysis dataset, and a manually tagged dataset.
4. The method as claimed in claim 3, wherein the pre-training of the processing unit [102] comprises:

- receiving, by the processing unit [102], the annotated social media dataset, the title dataset, and the sentiment-analysis dataset; and
- processing, by the processing unit [102], the annotated digital

platforms dataset, the title dataset, and the sentiment-analysis dataset, wherein the processing includes at least one of a lower casing task, a masking data task, a hashtag removal task, a business unit identification task, a promotional classification task, and a platform identification task.
5. The method as claimed in claim 1, wherein the aggregation action comprises a deduplication action performed by an aggregation unit [104].
6. The method as claimed in claim 1, wherein the bucketing action comprises:

- mapping, by a bucketing unit [106], the product-labels to one or more gross merchandise values; and
- classifying, by the bucketing unit [106], the predicted one or more product-labels into one or more buckets based on an overall-mentions criteria and a business unit criteria.
7. The method as claimed in claim 6, wherein the filtering action comprises:
- generating, by a filtering unit [108], a promotional ratio, a platform ratio and a growth count for each bucket of the one or more buckets; and
- filtering, by the filtering unit [108], one or more target product-labels based on a threshold value for the each bucket of the one or more buckets.

8. The method as claimed in claim 7, wherein the threshold value is a pre-defined value based on an accuracy score of the predicted one or more product-labels and a set of manually tagged labels.
9. The method as claimed in claim 1, wherein the method, prior to the predicting, by the pre-trained processing unit [102], the one or more product-labels, further comprises:

- sending, by the pre-trained processing unit [102] to a language model unit [112], the received set of data; and
- identifying, by the language model unit [112]; a set of promotional mentions of one or more product labels and a set of one or more non-promotional mentions of one or more product-labels.

10. The method as claimed in claim 1, wherein the method, prior to the predicting, by the pre-trained processing unit [102], the one or more product-labels, further comprises performing a business unit identification task, promotional classification task, and a platform identification task on the set of data.
11. The method as claimed in claim 1, wherein the method, prior to the predicting, by the pre-trained processing unit [102], the one or more product-labels, further comprises performing a lower casing task, a masking data task, a hashtag removal task on the set of data.
12. A system for identifying product-label from data on one or more social media platforms, the system comprising:
- a pre-trained processing unit [102] configured to:
o receive a set of data comprising one or more data points from
the one or more social media platforms; o predict one or more product-labels in the set of data; o perform an aggregation action, a bucketing action, and a
filtering action on the predicted one or more product-labels;
and o identify a set of target product-labels based on the aggregation
action, the bucketing action, and the filtering action; and
- a user interface [110] configured to:
o display the set of target product-labels from the data on one or more social media platforms.
13. The system as claimed in claim 12, wherein a search engine unit is
configured to:
- receive the identified set of target product-labels; and
- generate a decision regarding visibility of the set of target product-labels on the digital platform, wherein the decision regarding visibility is one of automatically increasing the visibility, automatically decreasing the visibility, and no change in the visibility of the set of target product-labels on the digital platform.

14. The system as claimed in claim 12, wherein the pre-trained processing unit [102] is trained based on an annotated social media dataset, a title dataset, an sentiment-analysis dataset, and a manually tagged dataset.
15. The system as claimed in claim 14, wherein the processing unit [102], for the pre-training, is configured to:

- receive the annotated social media dataset, the title dataset, and the sentiment-analysis dataset; and
- process the annotated digital platforms dataset, the title dataset, and the sentiment-analysis dataset, wherein the processing includes at least one of a lower casing task, a masking data task, a hashtag removal task, a business unit identification task, a promotional classification task, and a platform identification task.

16. The system as claimed in claim 12, further comprising an aggregation unit [104] configured to perform the aggregation action, wherein the aggregation action comprises a deduplication action.
17. The system as claimed in claim 12, further comprising a bucketing unit [106] configured to perform the bucketing action, wherein the bucketing action comprises:

- mapping the product-labels to one or more gross merchandise values; and
- classifying the predicted one or more product-labels into one or more buckets based on an overall-mentions criteria and a business unit criteria.
18. The system as claimed in claim 17, further comprising a filtering unit [108]
configured to perform the filtering action, wherein the filtering action
comprises:
- generating a promotional ratio, a platform ratio and a growth count for each bucket of the one or more buckets; and
- filtering one or more target product-labels based on a threshold value for the each bucket of the one or more buckets.
19. The system as claimed in claim 18, wherein the threshold value is a pre-

defined value based on an accuracy score of the predicted one or more product-labels and a set of manually tagged labels.
20. The system as claimed in claim 12, wherein the system further comprises a
language model unit [112], wherein the language model unit [112], prior to
the predicting, by the pre-trained processing unit [102], the one or more
product-labels, is configured to:
- receive, from the pre-trained processing unit [102], the received set of data; and
- identify a set of promotional mentions of one or more product labels and a set of one or more non-promotional mentions of one or more product-labels.

21. The system as claimed in claim 12, wherein the pre-trained processing unit [102], prior to the predicting the one or more product-labels, is further configured to perform a business unit identification task, promotional classification task, and a platform identification task on the set of data.
22. The system as claimed in claim 12, wherein the pre-trained processing unit [102], prior to the predicting the one or more product-labels, is further configured to perform a lower casing task, a masking data task, a hashtag removal task on the set of data.