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Methodology for Text Classification using Manually Created Corpora-based Sentiment Dictionary

Abstract

This paper presents the methodology of Textual Content Classification, which is based on a combination of algorithms: preliminary formation of a contextual framework for the texts in particular problem area; manual creation of the Hierarchical Sentiment Dictionary (HSD) on the basis of a topically-oriented Corpus; tonality texts recognition via using HSD for analysing the documents as a collection of topically completed fragments (paragraphs). For verification of the proposed methodology, a case study of Polish-language film reviews Corpora was used. The main scientific contributions of this research are: writing style of the analyzed text determines the possibility of adaptation of the Texts Classification algorithms; Hierarchically-oriented Structure of the HSD allows customizing the classification process to qualitative recognition of text tonality in the context of individual paragraphs topics; texts of Persuasive style most often are initially empowered by authors with a certain tonality. The tone, expressed in the author's opinion, effects the qualitative indicators of sentiment recognition. Negative emotions of the author usually reduce the level of vocabulary variability as well as the variety of topics raised in the document but simultaneously increase the level of unpredictability of words contextually used with both positive and negative emotional coloring

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Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Title of issue:
Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management strony 1 - 9
Language:
English
Publication year:
2018
Bibliographic description:
Rizun N., Waloszek W.: Methodology for Text Classification using Manually Created Corpora-based Sentiment Dictionary// Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management/ 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management : , 2018, s.1-9
DOI:
Digital Object Identifier (open in new tab) 10.5220/0006932602120220
Bibliography: test
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