OPINION MINING SYSTEM OF CUSTOMER REVIEWS BY USING FEATURE EXTRACTION

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.