14. A method comprising: receiving a feeling classification request from an application executing on a client device, feeling classification request comprising text data elements; in response, generating and transmitting a feeling classification response by: determining grammatical and semantic structure of the set of text data elements using a reverse sentence reconstruct (RSR) utility; generating sentence vectorization technique (SVT) models using a SVT utility; storing the SVT models, a labelled text corpus and a slang and spelling dictionary; generating a syntactic text tree with the text data elements using a parsing component of the RSR utility; and classifying feeling of the text data elements in the syntactic text tree using a classification component of the RSR utility.
14. A method comprising: receiving a feeling classification request from an application executing on a client device, feeling classification request comprising text data elements; in response, generating and transmitting a feeling classification response by: determining grammatical and semantic structure of the set of text data elements using a reverse sentence reconstruct (RSR) utility; generating sentence vectorization technique (SVT) models using a SVT utility; storing the SVT models, a labelled text corpus and a slang and spelling dictionary; generating a syntactic text tree with the text data elements using a parsing component of the RSR utility; and classifying feeling of the text data elements in the syntactic text tree using a classification component of the RSR utility. 16. The method of claim 14 , further comprising: for each word of the text data elements, obtaining a word vector from a parsing SVT model of the SVT utility; for each word vector: calculating, using a parsing combination matrix and a parsing probability vector, a probability of how well the word vector combines with neighbouring word vectors; and generating a phrase vector from the parsing combination matrix by combining the word vector with the neighbouring word vector with the highest probability; wherein the calculation and generation are repeated by treating each new phrase vector as a word vector to generate syntactic text tree of nodes representing a word or phrase vector; computing a part-of-speech matrix; for each node in the syntactic text tree: calculating a confidence score using the part-of-speech matrix, the confidence score providing a list of values representing a probability of how likely each part-of-speech can represent the word or phrase vector at the node; assigning a part-of-speech to the node based on the highest probability in the confidence score determining whether the confidence score is higher than a threshold; and outputting a syntactic text tree syntactic text tree with each node labeled with its corresponding part-of-speech.