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

Belal Abdullah Hezam Murshed1,∗, Hasib Daowd Esmail Al-ariki2,†, Suresha Mallappa3,‡
1,3 University of Mysore, Department of Studies in Computer Science, Mysore, Karnataka, India
2 Sana’a Community College,Department of Computer Networks Engineering and Technologies, Sana’a, Yemen
† hasibalariki@gmail.com
‡ sureshasuvi@gmail.com
* Corresponding Author: Belal Abdullah Hezam Murshed,

Computer Systems Science and Engineering 2020, 35(6), 495-512. https://doi.org/10.32604/csse.2020.35.495

Abstract

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 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.

Keywords

Sentence Semantic Similarity, Word Semantic Similarity, Natural Language Processing, Twitter, Big Data.

Cite This Article

B. Abdullah Hezam Murshed, H. Daowd Esmail Al-ariki and S. Mallappa, "Semantic analysis techniques using twitter datasets on big data: comparative analysis study," Computer Systems Science and Engineering, vol. 35, no.6, pp. 495–512, 2020.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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