Paper Info
Title
Experimental Analysis of the Relevance of Features and Effects on Gender Classification Models for Social Media Author Profiling
Abstract
Automatic user profiling from social networks has become a popular task due to its commercial applications (targeted advertising, market studies...). Automatic profiling models infer demographic characteristics of social network users from their generated content or interactions. Users' demographic information is also precious for more social worrying tasks such as automatic early detection of mental disorders. For this type of users' analysis tasks, it has been shown that the way how they use language is an important indicator which contributes to the effectiveness of the models. Therefore, we also consider that for identifying aspects such as gender, age or user's origin, it is interesting to consider the use of the language both from psycho-linguistic and semantic features. A good selection of features will be vital for the performance of retrieval, classification, and decision-making software systems. In this paper, we will address gender classification as a part of the automatic profiling task. We show an experimental analysis of the performance of existing gender classification models based on external corpus and baselines for automatic profiling. We analyse in-depth the influence of the linguistic features in the classification accuracy of the model. After that analysis, we have put together a feature set for gender classification models in social networks with an accuracy performance above existing baselines.
Year
DOI
Venue
2021
10.5220/0010431901030113
ENASE: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING
Keywords
DocType
Citations 
Gender Classification, Author Profiling, Feature Relevance, Social Media
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Paloma Piot-Perez-Abadin100.68
Patricia Martín-Rodilla200.68
Javier Parapar300.34