Comparison of Text Mining Techniques for Service Aspect Extraction

One of the main goals of service managers is to determine the strengths and weaknesses of the service delivery and to improve the service experience continually. Online feedback provided by customers offers service managers a valuable source of information to help achieve these goals. Despite the usefulness of reading such comments, service managers do not usually have sufficient resources to read all feedback thoroughly. Deriving concisely and focused managerially useful insights from online user content without wasting resources calls for an automated extraction technique. In this paper, we examine three different techniques for mining the service aspects from the same online data. The used techniques require a different degree of processing and provide a different level of information for service managers. The first technique retrieves nouns from the reviews, weights them and assesses the nature of an aspect, whether it is positive or negative, according to an overall user rating. The second technique counts co-occurrence statistics of nouns and adjectives and assesses them in the same way as the first technique. The third technique differs from the others in a deepness of text processing and its representation in a machine. A method parses grammatical dependencies between words and analyses the resulted trees by patterns. The third technique does not require rating scales to assess the nature of a service aspect. The grammatical dependencies involve sentence negation. So, the nature of an aspect can be extracted from the text directly. The data source for this paper is a major online review site that collects and presents customers’ opinions on different hotel and travel related services. The results show that the grammatical dependencies technique provides the most meaningful output but requires a careful extraction pattern setting. The outputs of the other two techniques are not ready for direct use to inform service managers.