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A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts

Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts t...

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Main Authors:	Ali Muttaleb, Hasan, Rassem, Taha H., Noorhuzaimi@Karimah, Mohd Noor, Ahmed Muttaleb, Hasan
Format:	Conference or Workshop Item
Language:	English
Published:	Springer 2020
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/28450/1/A%20Semantic%20Taxonomy%20for%20Weighting%20Assumptions%20to%20Reduce%20Feature%20Selection%20from%20Social%20Media%20and%20Forum%20Posts.pdf http://umpir.ump.edu.my/id/eprint/28450/ https://doi.org/10.1007/978-3-030-33582-3_39 https://doi.org/10.1007/978-3-030-33582-3_39
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spelling	my.ump.umpir.284502020-07-13T06:16:37Z http://umpir.ump.edu.my/id/eprint/28450/ A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts Ali Muttaleb, Hasan Rassem, Taha H. Noorhuzaimi@Karimah, Mohd Noor Ahmed Muttaleb, Hasan QA75 Electronic computers. Computer science QA76 Computer software Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts to clarify ambiguities in the lexical semantics of taxonomy in social media. It proposes a new knowledge-based semantic representation approach that can handle ambiguity and high dimensionality issues in text mining. The proposed approach consists of two main components, namely, a feature-based method for incorporating the relationships between lexical sources and a topic-based reduction method to overcome high dimensionality issues. These components help weight and reduce the relevant features of a concept. The proposed approach captures further lexical semantic similarity between words. It also evaluates the use of (https://wordnet.princeton.edu) WordNet 3.1 in text clustering and constant weighting assumption in the feature-based method used to select concepts/words from social media. To address ambiguity, the semantics of concepts with small feature subset size reduction are represented, and the performance of the semantic similarity measurement is improved. The proposed method evaluates word semantic similarity using the (https://github.com/alimuttaleb/semantictaxonomy/blob/master/mc30.txt) MC30 dataset in WordNet and obtains the following results for semantic representation: r = 0.82, p = 0.81, m = 0.81, and nz = 0.96. Springer 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28450/1/A%20Semantic%20Taxonomy%20for%20Weighting%20Assumptions%20to%20Reduce%20Feature%20Selection%20from%20Social%20Media%20and%20Forum%20Posts.pdf Ali Muttaleb, Hasan and Rassem, Taha H. and Noorhuzaimi@Karimah, Mohd Noor and Ahmed Muttaleb, Hasan (2020) A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts. In: 4th International Conference of Reliable Information and Communication Technology, IRICT 2019, 22-23 September 2019 , Johor Bahru, Malaysia. pp. 407-419., 1073. ISSN 2194-5357 ISBN 978-303033581-6 https://doi.org/10.1007/978-3-030-33582-3_39 https://doi.org/10.1007/978-3-030-33582-3_39
institution	Universiti Malaysia Pahang
building	UMP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang
content_source	UMP Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Ali Muttaleb, Hasan Rassem, Taha H. Noorhuzaimi@Karimah, Mohd Noor Ahmed Muttaleb, Hasan A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts
description	Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (https://github.com/alimuttaleb/Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts to clarify ambiguities in the lexical semantics of taxonomy in social media. It proposes a new knowledge-based semantic representation approach that can handle ambiguity and high dimensionality issues in text mining. The proposed approach consists of two main components, namely, a feature-based method for incorporating the relationships between lexical sources and a topic-based reduction method to overcome high dimensionality issues. These components help weight and reduce the relevant features of a concept. The proposed approach captures further lexical semantic similarity between words. It also evaluates the use of (https://wordnet.princeton.edu) WordNet 3.1 in text clustering and constant weighting assumption in the feature-based method used to select concepts/words from social media. To address ambiguity, the semantics of concepts with small feature subset size reduction are represented, and the performance of the semantic similarity measurement is improved. The proposed method evaluates word semantic similarity using the (https://github.com/alimuttaleb/semantictaxonomy/blob/master/mc30.txt) MC30 dataset in WordNet and obtains the following results for semantic representation: r = 0.82, p = 0.81, m = 0.81, and nz = 0.96.
format	Conference or Workshop Item
author	Ali Muttaleb, Hasan Rassem, Taha H. Noorhuzaimi@Karimah, Mohd Noor Ahmed Muttaleb, Hasan
author_facet	Ali Muttaleb, Hasan Rassem, Taha H. Noorhuzaimi@Karimah, Mohd Noor Ahmed Muttaleb, Hasan
author_sort	Ali Muttaleb, Hasan
title	A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts
title_short	A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts
title_full	A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts
title_fullStr	A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts
title_full_unstemmed	A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts
title_sort	semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts
publisher	Springer
publishDate	2020
url	http://umpir.ump.edu.my/id/eprint/28450/1/A%20Semantic%20Taxonomy%20for%20Weighting%20Assumptions%20to%20Reduce%20Feature%20Selection%20from%20Social%20Media%20and%20Forum%20Posts.pdf http://umpir.ump.edu.my/id/eprint/28450/ https://doi.org/10.1007/978-3-030-33582-3_39 https://doi.org/10.1007/978-3-030-33582-3_39
_version_	1672610896615047168
score	13.211869

A semantic taxonomy for weighting assumptions to reduce feature selection from social media and forum posts

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