The Utilization of Filter on Object-based Opinion Mining in Tourism Product Reviews

Abstract

The quality of a tourism products can be assessed using several aspects or objects due to their unique characteristics. The information related to the object can be extracted using object-based opinion mining. Based on the previous research, the implementation of Natural Language Processing (NLP) rules on object-based opinion mining for determining the orientation of the semantic objects showed good result. However, the performance of the objects extraction should be improved. In this study, researchers apply a filter on the objects’ extraction process of the hotel and restaurant review data. The utilization of data filter in object-based opinion mining succeeded in obtaining better objects’ extraction result due to the utilization of filter that eliminate the unrelated object. The application of filter in the process of objects extraction improve the precision of frequent object approach from 45.7% to 64.49% on of the hotel review and from 44.82% to 64.61% on the restaurant review. For frequent and infrequent approach, the precision was increased from 22.33% to 63.02% on the hotel review and from 21.6% to 65.4% on the restaurant review. For overall extracted object, the usage of filter got better result compared to non-filter classification process. The filtered object approach gave 56.85% accuracy, 60.91% precision, and 79.93% recall on the hotel review, and got 58.85% accuracy, 63.26% precision, and 84.14% recall on the restaurant review.