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Weighted aspect based sentiment analysis using extended OWA operators and Word2Vec for tourism

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Abstract

The tourism industry stimulates business revenues and economic activities across the globe. Effective analysis of enormous tourism reviews boosts both service quality and growth of industries. Aspect-based sentiment analysis (ABSA) has shown a prominent role in the aspect segmentation and sentiment ratings that obtains overall feedbacks and individual aspect feedback. In this regard, researchers are using Artificial Neural Network (ANN) for ABSA model learning. In addition to ANN, the state-of-the-art sentiment rating models adopted arithmetic mean (AM) of word embedding vectors and considered equal weightage to all aspects and reviews. But in real-world circumstances, these aspects and aspect reviews do not exhibit equal importance. They may vary from user to user and cannot be given equal weights. This is the first sentiment aggregation research that considers overall sentiment rating is consensus value from sentiment of its aspects and each aspect sentiment is the majority’s opinion associated sentences and their words. The proposed multi-layer knowledge representation architecture addresses this concept by using Word2Vec and extended families of the Ordered Weighted Average (OWA) operators. The novel approach signifies the weighted degree of importance for opinions and aspects using majority additive OWA (MAOWA), selective majority additive OWA (SMAOWA), and weighted selective aggregated majority OWA (WSAMOWA) operators. In addition to this, the proposed model also considers explicit and implicit aspect segmentation for review files, incorporates the meaning of slang internet words, and location-based geospatial rating analysis. Experimentation and evaluation conducted on TripAdvisor, Booking.com, Datafiniti tourism datasets show improvement in RMSE 14.68%, 59.03% and 12.97% and in Pearson correlation 30.63%, 23.34% and 32.61% respectively.

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Notes

  1. https://www.tripadvisor.com/

  2. https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe?select=Hotel_Reviews.csv

  3. https://www.kaggle.com/datafiniti/comments?select=Datafiniti_Hotel_Reviews_Jun19.csv

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  1. NSUT East Campus (erstwhile A.I.A.C.T.R.), Affiliated to Guru Gobind Singh Indraprastha University, Delhi, India

    Sayani Ghosal

  2. Netaji Subhas University of Technology, Delhi, India

    Amita Jain

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  1. Sayani Ghosal
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Ghosal, S., Jain, A. Weighted aspect based sentiment analysis using extended OWA operators and Word2Vec for tourism. Multimed Tools Appl 82, 18353–18380 (2023). https://doi.org/10.1007/s11042-022-13800-4

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