The influence of feature representation of text on the performance of document classification

Martinčić-Ipšić, Sanda (author), Miličić, Tanja (author), Todorovski, Ljupčo (author)

In this paper we perform a comparative analysis of three models for a feature representation of text documents in the context of document classification. In particular, we consider the most often used family of bag-of-words models, the recently proposed continuous space models word2vec and doc2vec, and the model based on the representation of text documents as language networks. While the bag-of-word models have been extensively used for the document classification task, the performance of the other two models for the same task have not been well understood. This is especially true for the network-based models that have been rarely considered for the representation of text documents for classification. In this study, we measure the performance of the document classifiers trained using the method of random forests for features generated with the three models and their variants. Multi-objective rankings are proposed as the framework for multi-criteria comparative analysis of the results. Finally, the results of the empirical comparison show that the commonly used bag-of-words model has a performance comparable to the one obtained by the emerging continuous-space model of doc2vec. In particular, the low-dimensional variants of doc2vec generating up to 75 features are among the top-performing document representation models. The results finally point out that doc2vec shows a superior performance in the tasks of classifying large documents.

• Language: English
• Keywords: document segmentation, bag-of-words, word2vec, doc2vec, graph-of-words, complex networks, document classification
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FU - Faculty of Administration
• Publication status: Published
• Publication version: Version of Record
• Year: 2019
• Number of pages: 27 str.
• Numbering: Vol. 9, iss. 4, art. 743
• PID: 20.500.12556/RUL-131966
• UDC: 004:78
• ISSN on article: 2076-3417
• DOI: 10.3390/app9040743
• COBISS.SI-ID: 5274286
• Publication date in RUL: 07.10.2021

Record is a part of a journal

• Title: Applied sciences
• Shortened title: Appl. sci.
• Publisher: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 20.02.2019

Secondary language

• Language: Slovenian
• Keywords: strojno učenje, razvrščanje besedil, vreča besed, word2vec, doc2vec, graf besed, kompleksna omrežja

Projects

• Funder: Other - Other funder or multiple funders
• Funding programme: University of Rijeka
• Project number: 13.13.2.2.07
• Acronym: LangNet

• Funder: ARRS - Slovenian Research Agency
• Project number: P5-0093
• Name: Razvoj sistema učinkovite in uspešne javne uprave