Classifying and extracting data from Facebook posts for online persona identification

Large amount of user-generated data areposted online in social media platforms, including user preferences, dining and leisureactivities, events, news and personal blogs.This resulted in varying efforts to process social media data using NLP and ML algorithmsfor topic classification, sentiment analysis anddetection, and events classification. Such information are problematic to process, as theytend to be short, informal, inconsistent, andare highly contextualized. A series of tasks isinvolved from collecting, pre-processing, classification and extraction before social mediadata can be used. In this study, we built amulti-class classifier model to process Facebook posts in order to identify a user\u27s onlinepersona based on his/her preferences. Information extraction is then applied to find relevant data from the classified posts that can beused to generate a description of the user\u27s online persona. The classifier currently achievesan accuracy of 76.02% and an F1 score of73.10% using 10-fold cross validation from adataset containing 16,682 posts. © 2018 by the authors