Abstract
The number of Twitter users is increasing and the quantity of produced data is growing. Using this big data to analyze user behavior has become a very active field. The two key challenges of this paper are extracting data from Twitter and extracting topics from user tweets. The proposed approach uses data crawling to collect data from Twitter and a bunch of natural language processing techniques to extract information from the so collected data and build a dataset. Thereafter, we use K-means clustering and Latent Dirichlet Allocation to extract the prevalent topics from this dataset, as they are the most common in the literature. Our proposal is generic, it can be reused by scientists to annotate any text collection.
Keywords
Cite this article
@article{cai-ADSI-7,
author = {Chaima Messaoudi and Zahia Guessoum and Lotfi BenRomdhane},
title = {Topic Extraction in Social Networks},
journal = {Computing and Informatics},
volume = {41},
number = {1},
pages = {56--77},
year = {2022},
issn = {1335-9150},
doi = {10.31577/cai_2022_1_56}
}
TY - JOUR AU - Chaima Messaoudi AU - Zahia Guessoum AU - Lotfi BenRomdhane TI - Topic Extraction in Social Networks JO - Computing and Informatics VL - 41 IS - 1 SP - 56 EP - 77 PY - 2022 SN - 1335-9150 DO - 10.31577/cai_2022_1_56 UR - https://doi.org/10.31577/cai_2022_1_56 KW - Twitter KW - topic extraction KW - big data KW - data crawling KW - social networks ER -
Messaoudi, C., Guessoum, Z., & BenRomdhane, L. (2022). Topic Extraction in Social Networks. Computing and Informatics, 41(1), 56–77. https://doi.org/10.31577/cai_2022_1_56