Method and system for detecting personal life events of users

A system for detecting personal life events of users, the system comprising:

• one or more processors configured to:
• extract a first set of social media data, associated with one or more first categories, from one or more social media platforms over a communication network;
• transmit the extracted first set of social media data to one or more crowdworkers over a crowdsourcing platform for annotating the extracted first set of social media data based on one or more second categories;
• extract a set of features from each of the annotated first set of social media data, wherein the extracted set of features includes at least a set of dialogue features that is indicative of at least one or more sentiments in the annotated first set of social media data;
• train one or more first classifiers based on the extracted set of features associated with each of the annotated first set of social media data;
• extract a second set of social media data of one or more users, associated with the one or more first categories, from the one or more social media platforms over the communication network;
• and
• categorize the extracted second set of social media data into the one or more second categories by use of the trained one or more first classifiers, wherein the categorization is utilized to detect the personal life events of the one or more users.

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 detecting personal life events of users, wherein the computer program code is executable by one or more processors to:

• extract a first set of social media data, associated with one or more first categories, from one or more social media platforms over a communication network;
• transmit the extracted first set of social media data to one or more crowdworkers over a crowdsourcing platform for annotating the extracted first set of social media data based on one or more second categories;
• extract a set of features from each of the annotated first set of social media data, wherein the extracted set of features includes at least a set of dialogue features that is indicative of at least one or more sentiments in the annotated first set of social media data;
• train one or more first classifiers based on the extracted set of features associated with each of the annotated first set of social media data;
• extract a second set of social media data of one or more users, associated with the one or more first categories, from the one or more social media platforms over the communication network;
• and
• categorize the extracted second set of social media data into the one or more second categories by use of the trained one or more first classifiers, wherein the categorization is utilized to detect the personal life events of the one or more users.