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Cross-Domain Sentiment Classification via Polarity-Driven State Transitions in a Markov Model

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 631))

Abstract

Nowadays understanding people’s opinions is the way to success, whatever the goal. Sentiment classification automates this task, assigning a positive, negative or neutral polarity to free text concerning services, products, TV programs, and so on. Learning accurate models requires a considerable effort from human experts that have to properly label text data. To reduce this burden, cross-domain approaches are advisable in real cases and transfer learning between source and target domains is usually demanded due to language heterogeneity. This paper introduces some variants of our previous work [1], where both transfer learning and sentiment classification are performed by means of a Markov model. While document splitting into sentences does not perform well on common benchmark, using polarity-bearing terms to drive the classification process shows encouraging results, given that our Markov model only considers single terms without further context information.

Giacomo Domeniconi—This work was partially supported by the project “Toreador”.

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Notes

  1. 1.

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Authors and Affiliations

  1. DISI, Università Degli Studi di Bologna, Via Venezia 52, Cesena, Italy

    Giacomo Domeniconi, Gianluca Moro, Andrea Pagliarani & Roberto Pasolini

Authors
  1. Giacomo Domeniconi
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  2. Gianluca Moro
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  3. Andrea Pagliarani
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  4. Roberto Pasolini
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Corresponding author

Correspondence to Andrea Pagliarani .

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Editors and Affiliations

  1. Instituto de Telecomunicações/IST, Lisbon, Portugal

    Ana Fred

  2. Delft University of Technology, Delft, The Netherlands

    Jan L.G. Dietz

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. University of Reading, Reading, United Kingdom

    Kecheng Liu

  5. Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Domeniconi, G., Moro, G., Pagliarani, A., Pasolini, R. (2016). Cross-Domain Sentiment Classification via Polarity-Driven State Transitions in a Markov Model. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2015. Communications in Computer and Information Science, vol 631. Springer, Cham. https://doi.org/10.1007/978-3-319-52758-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-52758-1_8

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