Publication

Abstract : Languages shared by people differs due to diversity in their ethnicity, socioeconomic status, gender, language, religion, sexual orientation, geographical area, accents, pronunciation and word usages. This eventually fall into hypothesis that they follow unknown hidden pattern. By using this hypothesis, determining the class of a person such as age, gender, their personality and nativity has multiple applications in social media, forensic science, marketing analysis, e-commerce and e-security. This work advances the research on author profiling much further by overcoming existing language dependent, domain dependent and lexicon based author profiling methods by finding user’s sociolect aspects based on authors statistical pattern of semantics in context space. It proves to be a domain and language independent method in Author Profiling by nearing constant performance over English, Dutch and Spanish corpus.