Consumer opinions are one of the most valuable assets that enterprises have, and thus questionnaires are often employed to investigate the views of consumers. However, this approach requires a large amount of human labor and time, and, most importantly, it cannot automatically find out consumers' needs. However, many consumers now share their appraisals of products or services through electronic word-of-mouth (eWOM). Since these usually reflect consumer needs, and thus their demands, collecting and analyzing eWOM data has become a key task for many businesses. Nonetheless, current eWOM-related research focuses on its transmission, influence, issues, and marketing, and there seem to be very few studies that apply eWOM to develop consumer needs analysis systems. In order to effectively collect and analyze eWOM data, this study proposes a computer-based approach for analyzing consumer demands. The approach utilizes sentiment analysis to develop extraction methods for use with eWOM appraisals. It thus uses eWOM appraisals to find out consumer demands. This work integrates eWOM with information technology to develop an approach to computerize consumer needs analysis. It is expected that the results will help enterprises to improve the quality of their products and market competitiveness.

In the rapid development of the information technology age, many travelers search for travel articles through the Internet. These travel articles include the experience and knowledge of traveler, which can be used as a reference for tourism planning and attraction selection. At present, the most travel experience and knowledge is available in online travel reviews (OTR). OTR and eWOM (electronic word-of-mouth) contain a lot of knowledge of consumers and travelers. Many travelers often look for OTR content through virtual communities, blogs, and search engine, but the search results often cause information overload problems. In addition, through virtual communities, blogs, and search engines, an OTR search still requires using keywords. However, most travelers cannot know the name of the attraction; therefore, travelers cannot use the correct keywords to search. That causes travelers to be unable to get enough information from OTR and unable to make the best travel plan. Therefore, this study focuses on the ontology-based tourist knowledge representation and recommendation method. And the study is to search for popular attractions from the OTR content and construct a tourist knowledge structure for these travelers. When the tourists do not need to know the keywords of the popular attraction name, they just need to get their current location; and then ORT content will recommend the next attraction to the traveler, which helps the traveler make the correct travel decision. The evaluation result showed that the method proposed in this study can help the travelers to quickly make the travel decision and is better than the traditional searching methods.