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ARMLOWA: aspect rating analysis with multi-layer approach

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Abstract

Aspect-based sentiment rating analysis is a decisive industry application for consumers and service providers. This research needs an efficient tool that reflects various shades of sentiments. The overall rating computation generally considered the average of all aspect ratings and failed to consider greater emphasis on positive and negative aspects. This research presents a novel multi-layer approach with the basic ordered weighted average (OWA) operator named ARMLOWA. Implementation of OWA operator made comprehensive aggregation that tailored user opinion and intention, and this multi-layer approach has shown the model learning advantageously. The model includes aspect segmentation, aspect rating, and overall sentiment rating computation. The model has picked the hospitality domain review dataset with significant aspects, like value, room, location, cleanliness, and service. The model confers 9.18% decrease in root-mean-square error (RMSE) and 29.67%, 4.98% increase for aspect correlation-inside reviews (PAspect) and across reviews (PReview), respectively.

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Notes

  1. www.tripadvisor.com.

  2. www.tripadvisor.com.

  3. http://times.cs.uiuc.edu/wang296/Data.

  4. https://github.com/piskvorky/gensim/.

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Authors and Affiliations

  1. Guru Gobind Singh Indraprastha University, Delhi, India

    Sayani Ghosal

  2. Netaji Subhas University of Technology, Delhi, India

    Amita Jain

  3. Indira Gandhi Delhi Technical University For Women, Delhi, India

    Swati Sharma & Devendra Kumar Tayal

Authors
  1. Sayani Ghosal
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  2. Amita Jain
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  3. Swati Sharma
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  4. Devendra Kumar Tayal
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Correspondence to Sayani Ghosal.

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Ghosal, S., Jain, A., Sharma, S. et al. ARMLOWA: aspect rating analysis with multi-layer approach. Prog Artif Intell 10, 505–516 (2021). https://doi.org/10.1007/s13748-021-00252-4

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