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Feature Based Opinion Mining for Restaurant Reviews

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Advances in Signal Processing and Intelligent Recognition Systems (SIRS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 678))

Abstract

Product reviews or customer feedback has become a platform for retailers to plan marketing strategy and also for new customers to select their appropriate product. Since the trend of e-commerce is increasing, an amount of customer reviews also has been increased to a greater extent. Consequently, it becomes a tough task for retailers as well as customers to read the reviews associated with the product. Sentiment analysis resolves this issue by scanning through free text reviews and providing the opinion summary. However, it does not provide detailed information, such as features on which the product is reviewed. Feature-based sentiment analysis methods increases the granularity of sentiment analysis by analyzing polarity associated with features in the given free text. The main objective of this work is to design a system that predicts polarity at aspect level and to design a score calculating scheme that defines the extent of polarity. Obtained feature - level scores are summarized according to users’ priority of interest.

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Notes

  1. 1.

    Comparative opinions involve comparison with other similar objects. For example, “Price of this phone is expensive” is an example for a regular opinion, while “price of this phone is better than phone-x” is a comparative opinion.

  2. 2.

    Representative mention - a special word in the sentence.

  3. 3.

    Mentions are the words present in other sentences referring representative mention.

  4. 4.

    \(M_{FS}\) - Maximum feature score awarded to the entity feature with respect to the opinion words.

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Author information

Authors and Affiliations

  1. Department of Information and Communication Technology, Manipal Institute of Technology, Manipal University, Manipal, 576104, Karnataka, India

    Nithin Y.R & Poornalatha G.

Authors
  1. Nithin Y.R
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  2. Poornalatha G.
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Corresponding author

Correspondence to Poornalatha G. .

Editor information

Editors and Affiliations

  1. School of CS/IT, Indian Institute of Information Technology and Management, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada

    Sri Krishnan

  3. Department of Computer Science, University of Salamanca, Salamanca, Salamanca, Spain

    Juan Manuel Corchado Rodriguez

  4. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  5. Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Wroclaw, Poland

    Michal Wozniak

  6. Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, United Kingdom

    Dhiya Al-Jumeily

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Y.R, N., G., P. (2018). Feature Based Opinion Mining for Restaurant Reviews. In: Thampi, S., Krishnan, S., Corchado Rodriguez, J., Das, S., Wozniak, M., Al-Jumeily, D. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2017. Advances in Intelligent Systems and Computing, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-67934-1_27

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  • DOI: https://doi.org/10.1007/978-3-319-67934-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67933-4

  • Online ISBN: 978-3-319-67934-1

  • eBook Packages: EngineeringEngineering (R0)

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