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Dynamic Document Localization for Efficient Mining

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Sentimental Analysis and Deep Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1408))

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Abstract

Annotation is the process of adding notes about an entity such as documents, attributes, data repositories and data spaces to make them more visible and expressive. In probabilistic data models, it is used for generating values for attributes which could be used for generating a range of queries which are matched with a database schema. The proposed fuzzy document localization model (FDLM) lists out the top-k attributes by deriving a monotone fuzzy rank function based on query value \(Q_\mathrm{val}\) and content value \(C_\mathrm{val}\). The newly arrived documents are processed with the annotated documents which are conditionally modeled with ground truth attributes in a dynamic document categorization process. The semantic matches of attributes are identified by a pre-processed conceptualization framework this in turn increases the cardinality of result set. The system is biased with a biasing parameter \(\beta \) in order to maintain a balance with workload-based query value and database-oriented content value to set a selection bound over the range of accurate and approximate matches.

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Authors and Affiliations

  1. University Visvesvaraya College of Engineering, Bangalore University, Bengaluru, Karnataka, India

    P. Sijin & H. N. Champa

Authors
  1. P. Sijin
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  2. H. N. Champa
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Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal

    Subarna Shakya

  2. Intelligent Systems Research Centre, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Songkla University, Songkhla, Thailand

    Sinchai Kamolphiwong

  4. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

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Sijin, P., Champa, H.N. (2022). Dynamic Document Localization for Efficient Mining. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_2

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