Patent Document ID: 10073830
Application ID: 15109044

Base Claim:
1. A system comprising: at least one client computing device executing an application to transmit a set of text data elements as a feeling classification request; at least one computer processor in communication with the at least one computing device over a communications network to receive the feeling classification request, and in response, transmit a feeling classification response, the computer processor configuring a text analysis engine, a reverse sentence reconstruct (RSR) utility for determining grammatical and semantic structure of the set of text data elements, and a sentence vectorization technique (SVT) utility to generate SVT models, wherein the computer processor is configured to compute the feeling classification response using the RSR utility and SVT utility, wherein the RSR utility interacts with the SVT utility to provide a parsing component to generate a syntactic text tree with parts-of-speech for the text data elements and a classification component to classify feeling of the text data elements for the feeling classification response; and at least one data storage device storing the SVT models, a labelled text corpus and a slang and spelling dictionary.

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Claim 3:
3. The system of claim 1 , wherein the parsing component is configured to: for each word of the text data elements, obtain a word vector from a parsing SVT model of the SVT utility; for each word vector: calculate, using a parsing combination matrix and a parsing probability vector, a probability of how well the word vector combines with neighbouring word vectors; and generate 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; compute a part-of-speech matrix; for each node in the syntactic text tree: calculate 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; assign a part-of-speech to the node based on the highest probability in the confidence score determine whether the confidence score is higher than a threshold; and output a syntactic text tree with each node labeled with its corresponding part-of-speech.