Statystyczne metody klasyfikacji tekstów
In recent years, with the fast development of computer and Internet technologies, text-mining computer methods are becoming more and more important. Computer systems capacities can be further used in such areas as text summarization, information retrieval, text correcting, determining text subject,...
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| Formaat: | Online |
| Taal: | Pools |
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Wydawnictwo Uniwersytetu Łódzkiego
2026
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| Online toegang: | ONIX_20260612T144849_9788382207873_37 |
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| _version_ | 1869514610368839680 |
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| author | Idczak, Adam Korzeniewski, Jerzy |
| author_browse | Idczak, Adam Korzeniewski, Jerzy |
| author_facet | Idczak, Adam Korzeniewski, Jerzy |
| author_sort | Idczak, Adam |
| collection | Directory of Open Access Books |
| description | In recent years, with the fast development of computer and Internet technologies, text-mining computer methods are becoming more and more important. Computer systems capacities can be further used in such areas as text summarization, information retrieval, text correcting, determining text subject, machine text translation, creating lexicons, determining text sentiment. https://pl.wikipedia.org/wiki/Język_angielskiThis monograph is focused on sentiment analysis in the most popular meaning of this phrase i.e. on the sentiment of the whole document. The problems of binary classification (two document groups), staying away from external sources, using the training set but in the possibly smallest size, were emphasized. The monograph’s targets are: providing a comparative review of sentiment analysis methods to be found in literature, investigating the quality of selected methods of document sentiment classification in applications to Polish language written documents, proposing new methods which would upgrade the classification quality or possess other advantages. An original method with simple interpretation has been proposed which proved to be better than standard methods applied to classify English language documents, especially in the case of documents corpora with similar number of documents in both classes. The research was carried out on thirteen sets of documents from different independent sources. |
| format | Online |
| id | doab-20.500.12854ir-177501 |
| institution | Directory of Open Access Books |
| language | pol |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Wydawnictwo Uniwersytetu Łódzkiego |
| publisherStr | Wydawnictwo Uniwersytetu Łódzkiego |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1775012026-06-12T13:47:40Z Statystyczne metody klasyfikacji tekstów Idczak, Adam Korzeniewski, Jerzy Sentiment of document Document classification Machine learning methods Linear correlation SVM method Naïve Bayes thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics In recent years, with the fast development of computer and Internet technologies, text-mining computer methods are becoming more and more important. Computer systems capacities can be further used in such areas as text summarization, information retrieval, text correcting, determining text subject, machine text translation, creating lexicons, determining text sentiment. https://pl.wikipedia.org/wiki/Język_angielskiThis monograph is focused on sentiment analysis in the most popular meaning of this phrase i.e. on the sentiment of the whole document. The problems of binary classification (two document groups), staying away from external sources, using the training set but in the possibly smallest size, were emphasized. The monograph’s targets are: providing a comparative review of sentiment analysis methods to be found in literature, investigating the quality of selected methods of document sentiment classification in applications to Polish language written documents, proposing new methods which would upgrade the classification quality or possess other advantages. An original method with simple interpretation has been proposed which proved to be better than standard methods applied to classify English language documents, especially in the case of documents corpora with similar number of documents in both classes. The research was carried out on thirteen sets of documents from different independent sources. 2026-06-12T13:47:37Z 2026-06-12T13:47:37Z 2022 book ONIX_20260612T144849_9788382207873_37 9788382207873 9788382207866 https://directory.doabooks.org/handle/20.500.12854/177501 pol image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://press.uni.lodz.pl/index.php/wul/pl/catalog/book/1458 Wydawnictwo Uniwersytetu Łódzkiego electronic 10.18778/8220-786-6 In recent years, with the fast development of computer and Internet technologies, text-mining computer methods are becoming more and more important. Computer systems capacities can be further used in such areas as text summarization, information retrieval, text correcting, determining text subject, machine text translation, creating lexicons, determining text sentiment. https://pl.wikipedia.org/wiki/Język_angielskiThis monograph is focused on sentiment analysis in the most popular meaning of this phrase i.e. on the sentiment of the whole document. The problems of binary classification (two document groups), staying away from external sources, using the training set but in the possibly smallest size, were emphasized. The monograph’s targets are: providing a comparative review of sentiment analysis methods to be found in literature, investigating the quality of selected methods of document sentiment classification in applications to Polish language written documents, proposing new methods which would upgrade the classification quality or possess other advantages. An original method with simple interpretation has been proposed which proved to be better than standard methods applied to classify English language documents, especially in the case of documents corpora with similar number of documents in both classes. The research was carried out on thirteen sets of documents from different independent sources. 10.18778/8220-786-6 83bfe9c9-323d-4283-b087-d859fd9af314 9788382207873 9788382207866 electronic open access |
| spellingShingle | Sentiment of document Document classification Machine learning methods Linear correlation SVM method Naïve Bayes thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics Idczak, Adam Korzeniewski, Jerzy Statystyczne metody klasyfikacji tekstów |
| title | Statystyczne metody klasyfikacji tekstów |
| title_full | Statystyczne metody klasyfikacji tekstów |
| title_fullStr | Statystyczne metody klasyfikacji tekstów |
| title_full_unstemmed | Statystyczne metody klasyfikacji tekstów |
| title_short | Statystyczne metody klasyfikacji tekstów |
| title_sort | statystyczne metody klasyfikacji tekstow |
| topic | Sentiment of document Document classification Machine learning methods Linear correlation SVM method Naïve Bayes thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics |
| topic_facet | Sentiment of document Document classification Machine learning methods Linear correlation SVM method Naïve Bayes thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics |
| url | ONIX_20260612T144849_9788382207873_37 |
| work_keys_str_mv | AT idczakadam statystycznemetodyklasyfikacjitekstow AT korzeniewskijerzy statystycznemetodyklasyfikacjitekstow |