Open AccessSentiment Classification of Bank Clients’ Reviews Written in the Polish Language
Author(s) -
Adam Idczak
Publication year - 2021
Publication title -
acta universitatis lodziensis. folia oeconomica
Language(s) - English
Resource type - Journals
eISSN - 2353-7663
pISSN - 0208-6018
DOI - 10.18778/0208-6018.353.03
Subject(s) - naive bayes classifier , sentiment analysis , logistic regression , computer science , natural language processing , classifier (uml) , artificial intelligence , support vector machine , machine learning
It is estimated that approximately 80% of all data gathered by companies are text documents. This article is devoted to one of the most common problems in text mining, i.e. text classification in sentiment analysis, which focuses on determining the sentiment of a document. A lack of defined structure of the text makes this problem more challenging. This has led to the development of various techniques used in determining the sentiment of a document. In this paper, a comparative analysis of two methods in sentiment classification, a naive Bayes classifier and logistic regression, was conducted. Analysed texts are written in the Polish language and come from banks. The classification was conducted by means of a bag‑of‑n‑grams approach, where a text document is presented as a set of terms and each term consists of n words. The results show that logistic regression performed better.