Grouping of Customer Opinions Written in Natural Language Using Unsupervised Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F12%3A00198889" target="_blank" >RIV/62156489:43110/12:00198889 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Grouping of Customer Opinions Written in Natural Language Using Unsupervised Machine Learning

Popis výsledku v původním jazyce

Among one of the current and most topical tasks in the area of textual documents processing belongs the problem of automatic categorization. Clustering as the most common form of unsupervised learning enables automatic grouping of unlabeled documents into subsets called clusters. In this paper, the authors are concerned with results of clustering of very large electronic real-world data collections containing customers' reviews written freely, in English as a natural language. The reviews are automatically clustered into two groups that should contain either positive or negative reviews. The paper focuses on the analysis why certain reviews are assigned wrongly to a group containing mostly reviews of a different class. The assignment of a review into acertain cluster is based on its properties, i.e., on the words that appeared in the review. Thus, words appearing in incorrectly categorized reviews were analyzed. It was found that words that are important from the correct classificatio

Název v anglickém jazyce

Grouping of Customer Opinions Written in Natural Language Using Unsupervised Machine Learning

Popis výsledku anglicky

Among one of the current and most topical tasks in the area of textual documents processing belongs the problem of automatic categorization. Clustering as the most common form of unsupervised learning enables automatic grouping of unlabeled documents into subsets called clusters. In this paper, the authors are concerned with results of clustering of very large electronic real-world data collections containing customers' reviews written freely, in English as a natural language. The reviews are automatically clustered into two groups that should contain either positive or negative reviews. The paper focuses on the analysis why certain reviews are assigned wrongly to a group containing mostly reviews of a different class. The assignment of a review into acertain cluster is based on its properties, i.e., on the words that appeared in the review. Thus, words appearing in incorrectly categorized reviews were analyzed. It was found that words that are important from the correct classificatio

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 14th International Symposium on Symbolic and Numeric Algorithms for scientific Computing SYNASC 2012

ISBN

978-0-7695-4934-7

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

265-270

Název nakladatele

IEEE

Místo vydání

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Místo konání akce

Timisoara

Datum konání akce

1. 1. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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