Interest in applying Big Data to tourism is increasing, and automated sentiment analysis has been used to extract public opinion from various sources. This article evaluates the suitability of different types of automated classifiers for applications typical in tourism, hospitality, and marketing studies by comparing their performance to that of human raters. While the commonly used performance indices suggest that on easier-to-classify data sets machine learning methods demonstrate performance comparable to that by human raters, other performance measures such as Cohen’s kappa show that the results of machine learning are still inferior to manual processing. On more difficult and noisy data sets, automated analysis has poorer performance than human raters. The article discusses issues pertinent to selection of appropriate sentiment analysis software and offers a word of caution against using automated classifiers uncritically.
This study investigated the causal relationships between international tourists' perceived sustainability of Jeju Island, South Korea and environmentally responsible behavior, revisit intention, and positive word-of-mouth communication. Perceived sustainability was employed as a multidimensional construct comprised of economic, cultural, and environmental aspects. Data were collected from international tourists that visited Jeju Island. The results indicated that environmentally responsible behavior was influenced positively by cultural sustainability, and negatively by environmental sustainability. Revisit intention and positive word-of-mouth communication were significantly affected by the three dimensions of sustainability. Based on the findings, associated implications were suggested for sustainable destination management of Jeju Island.
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