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dc.contributor.authorIdczak, Adam Piotr
dc.description.abstractIt 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.en
dc.description.abstractSzacuje się, że około 80% wszystkich danych gromadzonych i przechowywanych w systemach informacyjnych przedsiębiorstw ma postać dokumentów tekstowych. Artykuł jest poświęcony jednemu z podstawowych problemów textminingu, tj. klasyfikacji tekstów w analizie sentymentu, która rozumiana jest jako badanie wydźwięku tekstu. Brak określonej struktury dokumentów tekstowych jest przeszkodą w realizacji tego zadania. Taki stan rzeczy wymusił rozwój wielu różnorodnych technik ustalania sentymentu dokumentów. W artykule przeprowadzono analizę porównawczą dwóch metod badania sentymentu: naiwnego klasyfikatora Bayesa oraz regresji logistycznej. Badane teksty są napisane w języku polskim, pochodzą z banków i mają charakter marketingowy. Klasyfikację przeprowadzono, stosując podejście bag‑of‑n‑grams. W ramach tego podejścia dokument tekstowy wyrażony jest za pomocą podciągów składających się z określonej liczby n wyrazów. Uzyskane wyniki pokazały, że lepiej spisała się regresja
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl
dc.relation.ispartofseriesActa Universitatis Lodziensis. Folia Oeconomica;353en
dc.subjectsentiment analysisen
dc.subjectopinion miningen
dc.subjecttext classificationen
dc.subjecttext miningen
dc.subjectlogistic regressionen
dc.subjectnaive Bayes classifieren
dc.subjectanaliza sentymentupl
dc.subjectklasyfikacja dokumentówpl
dc.subjectregresja logistycznapl
dc.subjectnaiwny klasyfikator Bayesapl
dc.titleSentiment Classification of Bank Clients’ Reviews Written in the Polish Languageen
dc.title.alternativeAnaliza sentymentu na podstawie polskojęzycznych recenzji klientów bankupl
dc.contributor.authorAffiliationUniversity of Łódź, Faculty of Economics and Sociology, Department of Statistical Methods Łódź, Polanden
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