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2022 | OriginalPaper | Buchkapitel

7. Leveraging Closed Patterns and Formal Concept Analysis for Enhanced Microblogs Retrieval

verfasst von : Meryem Bendella, Mohamed Quafafou

Erschienen in: Complex Data Analytics with Formal Concept Analysis

Verlag: Springer International Publishing

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Abstract

Social microblogging services have gained a significant interest for society during our decade. These online platforms offered by the web 2.0 showed up the emergence of a large amount of data, allowing users to produce, share and exchange various content. Twitter is one of the most popular microblogging sites used by people to find relevant posts that satisfy their information need (e.g., breaking news, popular trends, information about people of interest, etc). However, Twitter’s queries and messages are short and access to information is sometimes difficult because of the variety of published content and huge amount of data generated. In this context, it is difficult for the user to properly find the relevant information. The proposal work deals with the context of social information retrieval (SIR) and aims to improve tweets retrieval quality. Thus, we propose a query expansion method to expand users’ queries. The proposed approach is based on Formal Concept Analysis by extracting patterns from documents retrieved by the search system. Also, the method uses Word Embeddings to enrich the patterns by adding similar words. The final query is therefore given by merging the initial query with the extended query. We experiment and evaluate the proposed method on the TREC 2011 dataset containing approximately 16 million tweets and 49 queries. Results revealed the effectiveness of the proposed approach and show the interest of combining patterns and word embeddings for enhanced microblogs retrieval.

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Titel: Leveraging Closed Patterns and Formal Concept Analysis for Enhanced Microblogs Retrieval
verfasst von: Meryem Bendella
Mohamed Quafafou
Verlag: Springer International Publishing
Buch: Complex Data Analytics with Formal Concept Analysis
Print ISBN: 978-3-030-93277-0

Electronic ISBN: 978-3-030-93278-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-93278-7_7

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