Aspect Based Sentiment Analysis for Explicit and Implicit Aspects in Restaurant Review using Grammatical Rules, Hybrid Approach, and SentiCircle
Institut Teknologi Sepuluh Nopember, Rachmad Abdullah, Suhariyanto Suhariyanto, Institut Teknologi Sepuluh Nopember, Riyanarto Sarno, Institut Teknologi Sepuluh Nopember · International journal of intelligent engineering and systems · 2021
TripAdvisor is one of the most popular e-commerce platforms in the tourism sector in Indonesia.TripAdvisor give Traveler Choice Award every year in Indonesia through user reviews.However, online text-based reviews are often associated only with evaluation scores that do not pay attention to the context and meaningful content of the review itself, either explicitly or implicitly.Moreover, the sentence structure of the review can have an impact on the goal of the target sentiment which is nothing but an aspect of the review itself.This research discusses aspect based sentiment analysis for explicit and implicit aspects.This research starting with taking the TripAdvisor website restaurant product review dataset to measure customer satisfaction based on four aspect categories of Ambience, Food, Service, and Price.Furthermore, the aspect word extraction and opinion word extraction processes in the case of explicit sentences for simple, compound, complex, and compound-complex sentence structures are carried out using grammatical rule extraction.This research also works on implicit sentence cases for simple sentence structures.Aspect categorization process uses hybrid approach.Aspect and opinion keyword extraction process uses the ELMo-Wikipedia.Then WordNet and TF-ICF are used to expand the meaning of aspect and opinion that has been taken.The last stage is the aspect based sentiment analysis process, both explicit and implicit sentences using SentiCircle.This research can produce two evaluations of sentiment classification, namely positive and negative.The results of the aspect extraction obtained an evaluation of the aspect categorization for each precision 0.82, recall 0.87, and f1measure 0.86.Meanwhile, the results of the sentiment analysis showed that the respective evaluations for precision 0.87, recall 0.92, and f1-measure 0.89.