Sentence Objectivity Analysis in Social Media

As more and more people record their lives and actions in Social Media, it has become an invaluable resource for the study of
society’s opinions and sentiment. From individual life events (e.g., someone’s purchase of a digital camera) to collective events of
major societal impact (e.g., the onset of the COVID-19 pandemic), social media has it all recorded and covered. A crucial step in
understanding social media is separating objective sentences, which state facts, from subjective ones, stating opinions. While
much progress has been made in identifying subjective sentences in product reviews, applicable to the e-Commerce domain, not
as much work exists for other kinds of text. This project will help close this gap by developing Machine Learning methods capable
of adapting tools for subjectivity detection in product reviews to other domains, by leveraging linguistic patterns and already
existing datasets.

Faculty Supervisor:

Denilson Barbosa

Student:

Partner:

Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM)

Discipline:

Computer science

Sector:

Artificial Intelligence; Information and Communications Technology; New and Digital Media

University:

University of Alberta

Program: