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Authors: Nada A. GabAllah and Ahmed Rafea

Affiliation: Computer Science and Engineering Dept., The American University in Cairo, AUC Avenue, New Cairo and Egypt

Keyword(s): Topic Extraction, Clustering, Twitter, Feature-pivot.

Related Ontology Subjects/Areas/Topics: Social Media Analytics ; Society, e-Business and e-Government ; Web Information Systems and Technologies

Abstract: Extracting topics from textual data has been an active area of research with many applications in our daily life. The digital content is increasing every day, and recently it has become the main source of information in all domains. Organizing and categorizing related topics from this data is a crucial task to get the best benefit out of this massive amount of information. In this paper we are presenting a feature-pivot based approach to extract topics from tweets. The approach is applied on a Twitter dataset in Egyptian dialect from four different domains. We are comparing our results to a document-pivot based approach and investigate which approach performs better to extract the topics in the underlying datasets. By applying t-test on recall, precision, and F1 measure values for both approaches on different datasets from different domains we confirmed our hypothesis that feature-pivot approach performs better in extracting topics from Egyptian dialect tweets in the datasets in ques tion. (More)

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Paper citation in several formats:
GabAllah, N. and Rafea, A. (2019). Unsupervised Topic Extraction from Twitter: A Feature-pivot Approach. In Proceedings of the 15th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-386-5; ISSN 2184-3252, SciTePress, pages 185-192. DOI: 10.5220/0007959001850192

@conference{webist19,
author={Nada A. GabAllah. and Ahmed Rafea.},
title={Unsupervised Topic Extraction from Twitter: A Feature-pivot Approach},
booktitle={Proceedings of the 15th International Conference on Web Information Systems and Technologies - WEBIST},
year={2019},
pages={185-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007959001850192},
isbn={978-989-758-386-5},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Web Information Systems and Technologies - WEBIST
TI - Unsupervised Topic Extraction from Twitter: A Feature-pivot Approach
SN - 978-989-758-386-5
IS - 2184-3252
AU - GabAllah, N.
AU - Rafea, A.
PY - 2019
SP - 185
EP - 192
DO - 10.5220/0007959001850192
PB - SciTePress

- Science and Technology Publications, Lda.
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