International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management

Knowledge Discovery, Knowledge Engineering and Knowledge Management pp 212-227 | Cite as

Modeling Sentiment Polarity with Meta-features to Achieve Domain-Independence

  • Octavian Lucian Hasna
  • Florin Cristian Măcicăşan
  • Mihaela Dînşoreanu
  • Rodica Potolea
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 553)

Abstract

Opinion mining has become an important field of text mining with high impact in numerous real-world problems. The limitations most solutions have in case of supervised learning refer to domain dependence: a solution is specifically designed for a particular domain. Our method overcomes such limitations by considering the generic characteristics hidden in textual information. We identify the sentiment polarity of documents that are part of different domains by using a uniform, cross-domain representation. It relies on three classes of original meta-features that characterize the datasets belonging to various domains. We evaluate our approach using datasets extensively referred in the literature. The results for in-domain and cross-domain verification show that the proposed approach handles novel domains increasingly better as its training corpus grows, thus inducing domain-independence.

Keywords

Sentiment detection Meta-features Classification Text mining Evaluation Supervised learning Domain independence 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Octavian Lucian Hasna
    • 1
  • Florin Cristian Măcicăşan
    • 1
  • Mihaela Dînşoreanu
    • 1
  • Rodica Potolea
    • 1
  1. 1.Computer Science DepartmentTechnical University of Cluj-NapocaCluj-NapocaRomania

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