Časopisi po područjima
Politike i razmjena
Izvorni znanstveni članak
Detecting the Online Image of “Average” Restaurants on TripAdvisor
; University of Zagreb, Faculty of political science, Zagreb, Croatia
Collective intelligence can be interpreted as the actions of individuals that provide collective effects. In online spaces, the more user comments about a matter of discussion, the higher the potential that certain repeated points of view will be used as a story frame. This observation can be a very useful explanation for the value of user comments, reviews and the ratings in the field of public relations. Nowadays, it has become noticeable that many indecisive people who are thinking of buying a product or using a certain service rely on information left by users who already have some kind of experience with the product or service. This information has an effect on decision-making and taking action. In the case of contemporary PR, collective intelligence, facilitated through user comments/reviews, is involved in the image making process. This paper uses the idea of collective intelligence to measure restaurants’ online image, using sentiment analysis to gain insight to users’ attitudes and opinions. Image is interpreted as a short-term outcome of organizational activities that can be identified through individual attitudes and opinions in this study. The author uses sentiment analysis, the use of natural language processing applications, to examine user comments and reviews for restaurants in Dubrovnik rated as “average” on the website TripAdvisor. This paper tests the accuracy of sentiment analysis software, therefore the efficiency of automated sentiment analysis is compared to human sentiment analysis. The results indicate that sentiment analysis tools could be important instruments for the estimation of a positive, negative, or neutral sentiment and detection of organization’s online image.
collective intelligence; online image; sentiment analysis; user comments; public relations; TripAdvisor
Hrčak ID: 166850
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