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A Hybrid Approach to Review Mining–-Restaurant Data in Depth Analysis

Publication Type : Conference Paper

Publisher : Innovative Data Communication Technologies and Application

Source : Innovative Data Communication Technologies and Application, Springer Singapore, Singapore (2021)

Url : https://link.springer.com/chapter/10.1007/978-981-15-9651-3_67

ISBN : 9789811596513

Campus : Bengaluru

School : School of Engineering

Department : Electronics and Communication

Year : 2021

Abstract : The eatery is a growing market and along with it grows the competition. To stay on the top, one must have satisfied and happy customers and their reviews are significant for a successful business. Nowadays, restaurants need to take customer reviews into account to enhance the customer experience. In this paper, a hybrid methodology is proposed to overcome this problem, faced by the restaurants, using sentimental analysis on the reviews and differentiate the positive and negative aspects of the restaurant. This paper highlights the importance of machine learning algorithms and is used to find patterns in data that help to make wiser decisions and predictions. The sentiment of the reviews are classified into positive and negative, and the score of each sentiment is also measured. The proposed approach gives a classification accuracy of 84.76% which is better than the existing methods.

Cite this Research Publication : R. P. Babu, Sreenivas, S., VinayVarma, U. S., and Dr. N. Neelima, “A Hybrid Approach to Review Mining–-Restaurant Data in Depth Analysis”, in Innovative Data Communication Technologies and Application, Singapore, 2021.

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