An ensemble scheme based on language function analysis and feature engineering for text genre classification

• Published in: Journal of information science Vol. 44; no. 1; pp. 28 - 47
• Main Author: Onan, Aytuğ
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.02.2018; Bowker-Saur Ltd
• Subjects: Algorithms; Artificial intelligence; Authoring; Authorship; Bayesian analysis; Classification; Classification schemes; Empirical analysis; Feature extraction; Function analysis; Genre; Information retrieval; Machine learning; Natural language processing; Performance prediction; Regression analysis; Support vector machines; ensemble learning; language function analysis; Text genre
• ISSN: 0165-5515; 1741-6485
• DOI: 10.1177/0165551516677911

Text genre classification is the process of identifying functional characteristics of text documents. The immense quantity of text documents available on the web can be properly filtered, organised and retrieved with the use of text genre classification, which may have potential use on several other tasks of natural language processing and information retrieval. Genre may refer to several aspects of text documents, such as function and purpose. The language function analysis (LFA) concentrates on single aspect of genres and it aims to classify text documents into three abstract classes, such as expressive, appellative and informative. Text genre classification is typically performed by supervised machine learning algorithms. The extraction of an efficient feature set to represent text documents is an essential task for building a robust classification scheme with high predictive performance. In addition, ensemble learning, which combines the outputs of individual classifiers to obtain a robust classification scheme, is a promising research field in machine learning research. In this regard, this article presents an extensive comparative analysis of different feature engineering schemes (such as features used in authorship attribution, linguistic features, character n-grams, part of speech n-grams and the frequency of the most discriminative words) and five different base learners (Naïve Bayes, support vector machines, logistic regression, k-nearest neighbour and Random Forest) in conjunction with ensemble learning methods (such as Boosting, Bagging and Random Subspace). Based on the empirical analysis, an ensemble classification scheme is presented, which integrates Random Subspace ensemble of Random Forest with four types of features (features used in authorship attribution, character n-grams, part of speech n-grams and the frequency of the most discriminative words). For LFA corpus, the highest average predictive performance obtained by the proposed scheme is 94.43%.