Sentiment Analysis in Tourism
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Abstract
The important part to gather the information is always seems as what the people think. Users express their views and opinions regarding products and services. These opinions are subjective information which represents user’s sentiments, feelings or appraisal related to the same.Every day, millions of people travel around the globe for business, vacations, sightseeing, or other reasons. An astronomical amount of money is spent on tickets, accommodations, food, transportation, and entertainment. Tourism is an information-based business where there are two types of information flow. One flow of information is from the providers to the consumers or tourists. This is information about goods that tourists consume such as tickets, hotel rooms, entertainments, and so forth. The other flow of information which follows a reverse direction consists of aggregate information about tourists to service providers. In this Chapter we will discuss the information flow about the behaviour of tourists. When the aggregated data about the tourists is presented in the right way, analysed by the correct algorithm, and put into the right hands, it could be translated into meaningful information for making vital decisions by tourism service providers. Data mining can be a very useful tool for analysing tourismrelated data. TOURISM DATA MINING Usually two types of machine learning activities are common in tourism association learning and classification learning. In association learning, the learning method searches for associations or relationships between features of tourist behaviour. For example, the algorithm may try to find out if tourists who are interested in shopping also prefer to stay near the centre of a city. That is, there is no specific target variable in this type of data mining, and so this is popularly known as unsupervised learning. A second style of machine learning is classification learning. This learning scheme takes a set of classified examples from which it discovers a way of classifying unseen examples. This is a form of supervised learning, in which there is a specific target variable. For example, by using classification analysts may be interested to classify tourists into two groups’ high spenders and low spenders for luxury items. In this case the target variable isexpenditure on luxury items. Based on a set of demographic and other variables the classification algorithm will establish the specific attributes of a tourist that qualify them as a high spender or a low spender. Next, we describe the various machine learning techniques used in tourism data mining.