Method and system for data processing for real-time text analysis

A system for data processing for real-time text analysis, the system comprising:

• one or more processors configured to:
• receive text content, by utilizing one or more transceivers, from a plurality of user-computing devices associated with a plurality of users communicating with each other over a communication network, wherein the text content comprises at least a current text segment and a previous text segment;
• extract one or more first features from the current text segment, wherein at least a first feature of the one or more first features corresponds to a difference between timestamps associated with each of the current text segment and the previous text segment;
• categorize the current text segment into a predetermined category of one or more predetermined categories, based on at least the one or more first features, automatically by utilizing a classifier, wherein the classifier is trained based on at least the one or more first features extracted from annotated one or more historical text segments in historical text content, wherein the predetermined category is associated with an attribute of one or more attributes;
• and
• predict a likelihood of evolution of the attribute in the text content, based on the predetermined category associated with the current text segment, wherein the predicted likelihood is presented as a graphical item on a display screen.

A computer program product for use with a computer, the computer program product comprising a non-transitory computer readable medium, wherein the non-transitory computer readable medium stores a computer program code for data processing for real-time text analysis, wherein the computer program code is executable by one or more processors to:

• receive text content, by utilizing one or more transceivers, from a plurality of user-computing devices associated with a plurality of users communicating with each other over a communication network, wherein the text content comprises at least a current text segment and a previous text segment;
• extract one or more first features from the current text segment, wherein at least a first feature of the one or more first features corresponds to a difference between timestamps associated with each of the current text segment and the previous text segment;
• categorize the current text segment into a predetermined category from one or more predetermined categories, based on at least the one or more first features, automatically by utilizing a classifier, wherein the classifier is trained based on at least the one or more first features extracted from annotated one or more historical text segments in historical text content, wherein the predetermined category is associated with an attribute of one or more attributes;
• and
• predict a likelihood of evolution of the attribute in the text content, based on the predetermined category associated with the current text segment, wherein the predicted likelihood is presented as a graphical item on a display screen.