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Aspect and orientation-based sentiment analysis of customer feedback using mathematical optimization models

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Abstract

Sentiment analysis is a natural language processing method used to assess data's positivity, negativity, and neutrality. Several techniques were suggested as ways to solve the sentiment analysis task. This study presents a novel multi-criteria decision-making (MCDM) and game theory-based mathematical framework for the sentiment orientation of reviews. We propose two frameworks: sentiment orientation tagger modal (SOTM) and aspect-based ranking modal (ABRM). The SOTM consists of the simple additive weighting (SAW) technique and the principle of Nash equilibrium from game theory to deduce the tag for the review dataset. We identify a review's sentiment as positive, negative, or neutral. In ABRM, we rank the aspects of the review using the preference selection index (PSI). We propose an unsupervised sentiment classification model that combines context, rating, and emotion scores with a mathematical optimization model. The effectiveness of our proposed model is comparable to the state-of-the-art models, as demonstrated by experimental results on three benchmark review datasets. We also establish the significance of the results through statistical analysis. The proposed model ensures rationality and consistency. The novel combination of the MCDM and game theory model with the reviews' context, rating, and emotion scores creates a new paradigm in sentiment analysis. Also, the proposed model is generalizable and can analyze sentiment in many fields.

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Data availability

Zomato reviews: https://www.zomato.com/ncr/33-food-malviya-nagar-new-delhi/reviews Swiggy reviews: https://www.kaggle.com/code/residentmario/exploring-tripadvisor-uk-restaurant-reviews/notebook. Yelp reviews https://www.kaggle.com/datasets/omkarsabnis/yelp-reviews-dataset. TripAdvisor reviews https://www.kaggle.com/code/residentmario/exploring-tripadvisor-uk-restaurant-reviews/notebook.

Code availability

The code generated during the current study is available from the corresponding author on reasonable request.

Notes

  1. https://pypi.org/project/pymc/.

  2. https://pypi.org/project/text2emotion/.

  3. https://github.com/drvinceknight/Nashpy.

  4. From nltk tokenize import word_tokenize.

  5. From nltk.stem import WordNetLemmatizer.

  6. From nltk.corpus import stopwords.

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  1. Department of Applied Mathematics, Delhi Technological University, Delhi, India

    Neha Punetha & Goonjan Jain

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  1. Neha Punetha
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Correspondence to Goonjan Jain.

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Punetha, N., Jain, G. Aspect and orientation-based sentiment analysis of customer feedback using mathematical optimization models. Knowl Inf Syst 65, 2731–2760 (2023). https://doi.org/10.1007/s10115-023-01848-z

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