Abstract
The data used in the opinion can be either factual or subjective, or both. The subjective form includes a positive or negative view, whereas there are facts in the objective form. As the result of opinion mining, the subjectivity and objectivity of knowledge are described as created. The outcome may be either positive or negative or a combination of both. Machine learning helps the computer to behave without a specific task being specifically programmed. Many consumers will critically evaluate everything online, especially food and amenities in eateries, to show their modest viewpoint. These views are critical in the decision-making process, especially in an uncertain feedback pool. Manually evaluating and deriving genuine opinions from these evaluations is very tough despite the growing count of opinions that are accessible in every category. So, to solve this issue, an automated methodology is needed. Sentiment evaluations or opinion extraction are returned methodologies to evaluate and identify uncertain feedback subjects as either positive or negative in these reviews. Three different dimensions of recommender systems are present; document-based, sentence-based, and aspect-based. Report and statement-based opinion mining concentrates on the broader orientation of the review and does not identify more accurately the crucial aspects of all the extracted reviews. The subject of trend is thus view-based opinion mining, and the emphasis of this research is on the restaurant's feedback sector. Creative marketing strategies are planned using sentiment assessments and opinion mining. The sentiment classifications are managed through a smart method called Neuro-Fuzzy Sentiment Classification. It is an entirely automated method of sentiment estimation and prediction. It can be used to group textual data and then used to identify patterns about the contents of the textual information. The resultant data aids in creating autonomous systems that consider the input from customers and prepare restaurant strategies.
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Kumar, V.V., Raghunath, K.M.K., Muthukumaran, V. et al. Aspect based sentiment analysis and smart classification in uncertain feedback pool. Int J Syst Assur Eng Manag 13 (Suppl 1), 252–262 (2022). https://doi.org/10.1007/s13198-021-01379-2
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DOI: https://doi.org/10.1007/s13198-021-01379-2