A Term Weight Measure based Approach for Author Profiling
Karunakar Kavuri, M. S. Kavitha · 2022
Author Profiling (AP) is a task of determining the demographic features like Age, Gender, Nativity language, location, Personality traits etc. about the author of a document. AP is used in different applications such as marketing linguistic profile, security and forensic science. The researchers proposed different types of solutions to author profiling based on stylistic features, content based features and deep learning techniques. The content based features proved their significance to improve the performance of author profiles prediction. Several approaches faced a problem of high dimensionality of features when experimented with content based features. The researchers used feature selection algorithms to identify the important features for experimentation. In this work, a Term Weight Measure (TWM) based approach is proposed for author profiling problem. In this approach, the important features are identified by using Feature Selection (FS) algorithm. After features are identified, the next important task is representation of document with identified features. The documents are represented as vectors and computation of each feature value in the vector representation is another important research task. TWMs are used to determine the importance of a feature in the vector representation. In the proposed approach, we proposed a new TWM based on the way the terms are distributed in corpus of documents. The proposed term weight measure performance is compared with different existing TWMs. The PAN competition 2014 reviews dataset is used for age and gender prediction of the author. Two Machine Learning (ML) algorithms such as Random Forest (RF) and Support Vector Machine (SVM) are used to evaluate the proposed term weight measure based approach. The experimental results attained in this work for age and gender prediction are good when compared with several popular solutions to author profiling.