APPARATUS AND METHOD OF OPPORTUNITY CLASSIFICATION

1 . An apparatus for opportunity classification, comprising:

at least a processor; and

a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to:

receive at least a semantic element from a user input, wherein the user input comprises at least a media item;

generate, as a function of the at least a semantic element, a plurality of similar semantic elements;

generate, using thematic training data, an index classifier, wherein generating the index classifier comprises:

creating the thematic training data using data from a plurality of media items and a plurality of correlated themes; and

generating, by the processor, the index classifier using the thematic training data;

receive training data correlating semantic elements to normalized semantic elements, wherein normalized semantic elements are one or more words;

train a semantic machine learning model, wherein the semantic machine learning model is configured to input semantic elements and output normalized semantic elements; and

determine, as a function of the semantic machine learning model, normalized semantic elements;

extract from each media item of the plurality of media items a plurality of content elements;

identify a prevalence of at least an object on the at least a media item, wherein identifying the prevalence further comprises classifying, by an object classifier, each content element of the plurality of content elements to an object from a plurality of objects;

query an opportunity database for opportunities as a function of the plurality of similar semantic elements as a function of the index classifier, wherein the index classifier is configured to classify the at least a media item to a theme as a function of the prevalence of at least an object;

map at least a similar semantic element of the plurality of similar semantic elements to a semantic element of the opportunity database;

determine a normalized semantic element as a function of the mapping; and

mark an opportunity of the opportunity database as a function of the determined normalized semantic element.

2 . The apparatus of claim 1 , wherein the at least a processor is further configured to implement a fuzzy logic model to query the opportunity database.

3 . The apparatus of claim 1 , wherein the at least a processor is further configured to map at least a similar semantic element of the plurality of similar semantic elements of the opportunity database as a function of a semantic threshold.

4 . The apparatus of claim 1 , wherein the at least a processor is further configured to determine a normalized semantic element as a function of an optimization model.

5 . The apparatus of claim 1 , wherein querying an opportunity database further comprises querying a web crawler index.

6 . The apparatus of claim 1 , wherein the at least a processor is further configured to map the at least a semantic element from the user input to the determined normalized semantic element in a semantic element database.

7 . The apparatus of claim 6 , wherein the at least a processor is further configured to query the opportunity database as a function of the mapping of the at least a semantic element from the user input to the determined normalized semantic element of the semantic element database.

8 . The apparatus of claim 1 , wherein the at least a processor is further configured to generate a plurality of similar semantic elements utilizing a language processing module.

9 . The apparatus of claim 1 , wherein the at least a processor is further configured to map at least a similar semantic element of the plurality of similar semantic elements to a semantic element of the opportunity database as a function of a clustering algorithm.

10 . (canceled)

11 . A method of opportunity classification using at least a processor, comprising:

receiving at least a semantic element from a user input, wherein the user input comprises at least a media item;

generating, as a function of the at least a semantic element, a plurality of similar semantic elements;

generating, using thematic training data, an index classifier, wherein generating the index classifier comprises:

creating the thematic training data using data from a plurality of media items and a plurality of correlated themes; and

generating, by the processor, the index classifier using the thematic training data;

receiving training data correlating semantic elements to normalized semantic elements, wherein normalized semantic elements are one or more words;

training a semantic machine learning model, wherein the semantic machine learning model is configured to input semantic elements and output normalized semantic elements; and

determining, as a function of the semantic machine learning model, normalized semantic elements;

extracting, by the processor, from each media item of the plurality of media items a plurality of content elements;

identifying, by the processor, a prevalence of at least an object on the at least a media item, wherein identifying the prevalence further comprises classifying, by an object classifier, each content element of the plurality of content elements to an object from a plurality of objects;

querying an opportunity database for opportunities as a function of the plurality of similar semantic elements, as a function of the index classifier;

mapping at least a similar semantic element of the plurality of similar semantic elements to a semantic element of the opportunity database;

determining a normalized semantic element as a function of the mapping; and

marking an opportunity of the opportunity database as a function of the determined normalized semantic element.

12 . The method of claim 11 , wherein the at least a processor is further configured to implement a fuzzy logic model to query the opportunity database.

13 . The method of claim 11 , wherein the at least a processor is further configured to map at least a similar semantic element of the plurality of similar semantic elements of the opportunity database as a function of a semantic threshold.

14 . The method of claim 11 , wherein the at least a processor is further configured to determine a normalized semantic element as a function of an optimization model.

15 . The method of claim 11 , wherein querying an opportunity database further comprises querying a web crawler index.

16 . The method of claim 11 , wherein the at least a processor is further configured to map the at least a semantic element from the user input to the determined normalized semantic element in a semantic element database.

17 . The method of claim 16 , wherein the at least a processor is further configured to query the opportunity database as a function of the mapping of the at least a semantic element from the user input to the determined normalized semantic element of the semantic element database.

18 . The method of claim 11 , wherein the method further comprises generating a plurality of similar semantic elements utilizing a language processing module.

19 . The method of claim 11 , wherein the at least a processor is further configured to map at least a similar semantic element of the plurality of similar semantic elements to a semantic element of the opportunity database as a function of a clustering algorithm.

20 . (canceled)

21 . The system of claim 1 , wherein the at least a media item is a video file.

22 . The method of claim 11 , wherein the at least a media item is a video file.