UniNE at CLEF 2017: Author Profiling Reasoning

This paper describes and evaluates a supervised author profiling model. The suggested strategy can be adapted without any problem to various languages (such as Arabic, English, Spanish, and Portuguese). As features, we suggest using the m most frequent terms of the query text (isolated words and punctuation symbols with m at most 200). Applying a simple distance measure and looking at the nearest text profiles, we can determine the gender (with the nominal values “male” or “female”) and the language variety (e.g., in Spanish the nominal values “Argentina”, “Chile”, “Colombia”, “Mexico”, “Peru”, “Spain”, or “Venezuela”). The training and test data is available for Twitter tweets (PAN AUTHOR PROFILING task at CLEF 2017). An analysis of the top ranked terms from a feature selection method allows a better understanding of the proposed assignments and presents typical writing styles for each category.