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Keyword Extraction from Tweets Using Weighted Graph

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Cognitive Informatics and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 768))

Abstract

One of the most important tasks of sentiment analysis of twitter contents is automatic keyword extraction. Vector Space Model (VSM) is one of the most well-known keyword extraction techniques; however it has some limitation such as scalability and sparsity. Graph-based keyword extraction approach is used to overcome those limitations. This paper proposes an unsupervised graph-based keyword extraction method, called Keyword from Weighted Graph (KWG) which uses Node Edge (NE) rank centrality measure to calculate the importance of nodes closeness centrality measure to break the ties among the nodes. The proposed method is validated with two datasets: Uri Attack, and American Election. From the experimental results it is observed that the performances of the proposed method outperform the eigen vector centrality and the textrank centrality measures. The performances are shown in terms of precision, recall, and F-measure.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Silchar, India

    Saroj Kumar Biswas

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  1. Saroj Kumar Biswas
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Correspondence to Saroj Kumar Biswas .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Hyderabad, Telangana, India

    Pradeep Kumar Mallick

  2. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Rangpo, India

    Akash Kumar Bhoi

  4. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, United Kingdom

    Ahmed F. Zobaa

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Biswas, S.K. (2019). Keyword Extraction from Tweets Using Weighted Graph. In: Mallick, P., Balas, V., Bhoi, A., Zobaa, A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_47

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