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An Entity Extraction and Categorization Technique on Twitter Streams

Author

Listed:
  • 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)

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.

Suggested Citation

  • Senthil Kumar Narayanasamy & Maiga Chang, 2024. "An Entity Extraction and Categorization Technique on Twitter Streams," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 23(03), pages 1203-1228, May.
    • Handle: RePEc:wsi:ijitdm:v:23:y:2024:i:03:n:s0219622023500360
    DOI: 10.1142/S0219622023500360
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