Semantic Analysis Techniques using Twitter Datasets on Big Data: Comparative Analysis Study

Murshed, Belal Abdullah Hezam and Al-ariki, Hasib Daowd Esmail and Suresha, M. (2020) Semantic Analysis Techniques using Twitter Datasets on Big Data: Comparative Analysis Study. Computer Systems Science & Engineering, 35 (6). pp. 495-512. ISSN 0267-6192

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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, POS-based, 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 with Wu 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.

Item Type: Article
Uncontrolled Keywords: Sentence Semantic Similarity; Word Semantic Similarity; Natural Language Processing; Twitter; Big Data
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: Mr Umendra uom
Date Deposited: 30 Mar 2021 09:54
Last Modified: 15 Nov 2022 09:24

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