Abstract:
Due to the dramatic improvement of ecommerce, web sources which are
important for both potential customers and service providers rapidly emerge in
prediction and decision purposes. Opinion mining techniques become popular to
automatically process customer reviews by extracting features and user opinions
expressed over them. To overcome the task of manual scanning through the large
amount of one-by-one review, people have interested to automatically process the
various reviews and to provide the information which is useful for customers and
service providers. By applying dependency relations, it can properly identify the
semantic relationships between features and opinions of each review. It can find the
numeric score of all the features using SentiWordNet. This system is intended to
collect customer reviews from tourism field and then extract the related features and
opinions to rate the services. Finally, it can rank each agency according to the final
result of each review sentence. In this thesis, Standard Parser is used to generate the
features, opinions and the dependency relations for each trip review at the
preprocessing. The two methods of features extraction such as frequency-based
feature extraction and dependency grammar-based feature extraction are used to
extract the most relevant trip features. Moreover, SentiWordNet 3.0 is used to get the
positive score and negative score for each trip feature and then the system calculates
the total weight of the trip review by using these numeric scores. The objective of the
system is to rank the travel agencies according to the final weight of each travel
agency that is collected by adding the total weight of the trip reviews for that agency.
Therefore, the system implements efficiency and effectiveness in opinion mining to
express the reviewer’s opinion and feeling for next customers’ trip plans by using
features extraction. In this system, Tourism Reviews are applied as the case studies to
identify what elements of an agency affect sales most and what are the features the
customer like or dislike so that trip managers and agency owners can target on those
areas. The system is developed using Java language and MySQL to build the
database.
