Open Access

ARTICLE

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

Belal Abdullah Hezam Murshed^1,∗, Hasib Daowd Esmail Al-ariki^2,†, Suresha Mallappa^3,‡

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.

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Citations