Semantic Analysis Techniques using Twitter Datasets on Big Data: Comparative Analysis Study.
In: Computer Systems Science & Engineering, Jg. 35 (2020-11-01), Heft 6, S. 495-512
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Zugriff:
This paper conducts a comprehensive review of various word and sentence semantic similarity techniques proposed in the literature. Corpus-based, Knowledge-based, and Feature-based are categorized under word semantic similarity techniques. String and set-based, Word Order-based Similarity, POSbased, Syntactic dependency-based are categorized as sentence semantic similarity techniques. Using these techniques, we propose a model for computing the overall accuracy of the twitter dataset. The proposed model has been tested on the following four measures: Atish's measure, Li's measure, Mihalcea's measure with path similarity, and Mihalcea's measure withWu and Palmer's (WuP) similarity. Finally, we evaluate the proposed method on three real-world twitter datasets. The proposed model based on Atish's measure seems to offer good results in all datasets when compared with the proposed model based on other sentence similarity measures. [ABSTRACT FROM AUTHOR]
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Titel: |
Semantic Analysis Techniques using Twitter Datasets on Big Data: Comparative Analysis Study.
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Autor/in / Beteiligte Person: | Murshed, Belal Abdullah Hezam ; Al-ariki, Hasib Daowd Esmail ; Mallappa, Suresha |
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Zeitschrift: | Computer Systems Science & Engineering, Jg. 35 (2020-11-01), Heft 6, S. 495-512 |
Veröffentlichung: | 2020 |
Medientyp: | academicJournal |
ISSN: | 0267-6192 (print) |
DOI: | 10.32604/csse.2020.35.495 |
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