Analysis of data pre-processing methods for the sentiment analysis of reviews

Tuba Parlar; Ayşe Selma Ozel; Fei Song

doi:10.7494/csci.2019.20.1.3097

Authors

Tuba Parlar Head of the ComputerTechnologies Department Antakya Vocational School Hatay Mustafa Kemal University http://orcid.org/0000-0002-8004-6150
Ayşe Selma Ozel Head of the Computer Engineering Department Cukurova University Adana http://orcid.org/0000-0001-9201-6349
Fei Song School of Computer Science Department University of Guelph Ontario http://orcid.org/0000-0003-3036-9696

DOI:

https://doi.org/10.7494/csci.2019.20.1.3097

Keywords:

Data pre-processing, feature selection, sentiment analysis, text classification

Abstract

The aim of this study is to analyse the effects of data pre-processing methods for sentiment analysis and determine which of these pre-processing methods and their combinations are effective for English and an agglutinative language like Turkish. We also try to answer the research question “is there any difference between agglutinative and non-agglutinative languages in terms of pre-processing methods for sentiment analysis?” We find that the performance results for the English reviews are generally higher than for the Turkish reviews related to the differences between the two languages in terms of vocabularies, writing styles, and agglutinative property of the Turkish language.

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