@Article{csse.2020.35.495,
AUTHOR = {Belal Abdullah Hezam Murshed, Hasib Daowd Esmail Al-ariki, Suresha Mallappa},
TITLE = {Semantic Analysis Techniques using Twitter Datasets on Big Data: Comparative Analysis Study},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {35},
YEAR = {2020},
NUMBER = {6},
PAGES = {495--512},
URL = {http://www.techscience.com/csse/v35n6/40726},
ISSN = {},
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.},
DOI = {10.32604/csse.2020.35.495}
}