Automatic keyword extraction for localized tweets using fuzzy graph connectivity measures

With an upsurge in the use of social media, a tremendous amount of textual data is being generated, which is being used for applications like sentiment analysis, industry trend analysis, information retrieval etc. In this context, automatic keyword extraction is a crucial and useful task. Many graph...

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Bibliographic Details
Published in:Multimedia tools and applications Vol. 81; no. 30; pp. 42931 - 42956
Main Authors: Jain, Minni, Bhalla, Grusha, Jain, Amita, Sharma, Swati
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2022
Springer Nature B.V
Subjects:
1135T: Social Multimedia Processing
Apexes
Computer Communication Networks
Computer Science
Data mining
Data Structures and Information Theory
Datasets
Experimentation
Fuzzy sets
Graph theory
Information retrieval
Keywords
Multimedia Information Systems
Nodes
Special Purpose and Application-Based Systems
Trend analysis
Fuzzy centrality measures
Information retrieval
Extraction
Graph-based method
Location-based
Social media analysis
ISSN:1380-7501, 1573-7721
Online Access:Get full text
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Summary:With an upsurge in the use of social media, a tremendous amount of textual data is being generated, which is being used for applications like sentiment analysis, industry trend analysis, information retrieval etc. In this context, automatic keyword extraction is a crucial and useful task. Many graph - based methods have been proposed which consider co-occurrence as edge weight, but these methods neglect the semantic relations between words. This paper proposes an automatic keyword extraction method for tweets from Twitter that represents text as a fuzzy graph and applies fuzzy centrality measures to find relevant keywords (vertices). Proposed work, F-GAKE (fuzzy graph automatic keyword extraction) takes belongingness of two words concerning the theme of the dataset into consideration and provides a fuzzy edge weight. It also considers node weight which incorporates the position of the words, frequency, importance, strength of neighbours and distance from the central node. It then uses fuzzy degree centrality, fuzzy betweenness, fuzzy PageRank and fuzzy Node and Edge (NE) Rank measures which provide relevant keywords. It is further extended to extract keywords for localized trending topics from Twitter. For experimentation, various Twitter datasets are used and results show that F-GAKE performs better than the state-of-the-art approaches for automatic keyword extraction for short messages, such as tweets.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-021-11893-x