Sentiment analysis model to emphasize the impact of online reviews in healthcare industry

Abstract

Abstract Purpose The purpose of this paper is to develop a systematic approach to extract users' feelings and emotions about their experiences in hospitals from online reviews and rank the places using multi-criteria decision making (MCDM) techniques based on the aggregated sentiment score. Design/methodology/approach The proposed model used a linguistic approach to extract the sentiment words from the free text. It used term frequency-inverse document frequency values to represent features of various places in bag-of-words format. Sentiment dictionary is used to calculate senti-scores. It used different MCDM techniques like simple additive weight and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods for ranking hospitals based on their aggregated senti-score. Findings Statistical correlation analysis between the rankings of places reveals that the TOPSIS method is the most suitable ranking technique among other MCDM techniques. By improving the senti-score, one can bring their enterprise to the top position. Research limitations/implications Data set is collected from different websites like Twitter, Facebook, etc., for various services/features. Moderate amount of reviews are collected for each place. But not all users give their views on the social media websites. It would be essential to collect responses from all the customers who avail different services at different places. Practical implications The sentiment analysis model proposed in this paper enables B2C and C2C commerce. Business may take suitable measures to overcome their issues/problems raised by the consumer. Consumers can share and educate other consumers about their experiences. Social implications The development of internet has strong influence in all types of industries like healthcare. The availability of internet has changed the way of accessing the information and sharing their experience with others. This paper recognizes the use and impact of social media on the healthcare industry by analyzing the users' feelings expressed in the form of free text. A suitable decision-making technique is applied to rank the places, which enables the users to plan their treatment place in a better way. Originality/value The paper develops a novel approach by applying the TOPSIS method to rank the different alternative places of the healthcare industry by using the senti-score derived from the users' feelings, emotions and experiences expressed in the form of free text.