An Entity Extraction and Categorization Technique on Twitter Streams

Senthil Kumar Narayanasamy () and Maiga Chang
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Senthil Kumar Narayanasamy: School of Information Technology & Engineering, VIT, Vellore, Tamil Nadu, India
Maiga Chang: School of Computing and Information Systems, Athabasca University, Athabasca, AB, Canada3Multidisciplinary Academic Research Center, National Dong Hwa University, Hualien, Taiwan

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 03, 1203-1228

Abstract: As social media platforms have gained huge momentum in recent years, the amount of information generated from the social media sites is growing exponentially and gives the information retrieval systems a great challenge to extract the potential named entities. Researchers have utilized the semantic annotation mechanism to retrieve the entities from the unstructured documents, but the mechanism returns with too many ambiguous entities. In this work, the DBpedia knowledge base is adopted for entity extraction and categorization. To achieve the entity extraction task precisely, a two-step process is proposed: (a) train the unstructured datasets with Word2Vec and classify the entities into their respective categories. (b) crawl the web pages, forums, and other web sources to identifying the entities that are not present in the DBpedia. The evaluation shows the results with more precision and promising F1 score.

Keywords: Named entity recognition; Word2Vec; LDA; DBpedia; Tweets; knowledge base (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219622023500360

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