A Semantic Taxonomy for Weighting Assumptions to Reduce Feature Selection from Social Media and Forum Posts

  • Ali Muttaleb HasanEmail author
  • Taha Hussein Rassem
  • Noorhuzaimi Mohd Noor
  • Ahmed Muttaleb Hasan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)


Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of ( synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts to clarify ambiguities in the lexical semantics of taxonomy in social media. It proposes a new knowledge-based semantic representation approach that can handle ambiguity and high dimensionality issues in text mining. The proposed approach consists of two main components, namely, a feature-based method for incorporating the relationships between lexical sources and a topic-based reduction method to overcome high dimensionality issues. These components help weight and reduce the relevant features of a concept. The proposed approach captures further lexical semantic similarity between words. It also evaluates the use of ( WordNet 3.1 in text clustering and constant weighting assumption in the feature-based method used to select concepts/words from social media. To address ambiguity, the semantics of concepts with small feature subset size reduction are represented, and the performance of the semantic similarity measurement is improved. The proposed method evaluates word semantic similarity using the ( MC30 dataset in WordNet and obtains the following results for semantic representation: r = 0.82, p = 0.81, m = 0.81, and nz = 0.96.


Semantic taxonomy Feature-based method Semantic representation Feature selection Gloss Social media MC30 



This work is supported by the University Malaysia Pahang (UMP) via Research Grant UMP RDU1803141.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ali Muttaleb Hasan
    • 1
    Email author
  • Taha Hussein Rassem
    • 1
  • Noorhuzaimi Mohd Noor
    • 1
  • Ahmed Muttaleb Hasan
    • 1
  1. 1.Faculty of Computing (FKOM)University Malaysia PahangGambang, KuantanMalaysia

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