TY - JOUR
T1 - Aspect-Based Sentiment Analysis for Sentence Types with Implicit Aspect and Explicit Opinion in Restaurant Review Using Grammatical Rules, Hybrid Approach, and SentiCircle
AU - Suhariyanto,
AU - Abdullah, Rachmad
AU - Sarno, Riyanarto
AU - Fatichah, Chastine
N1 - Publisher Copyright:
© 2021. All Rights Reserved.
PY - 2021
Y1 - 2021
N2 - Sentiment analysis can provide rough recommendations in the form of sentiment from a collection of reviews or can provide recommendations in more detail about sentiment in a particular aspect called aspect-based sentiment analysis (ABSA). Sentiment analysis based on many aspects has been carried out but its accuracy is still being developed. In previous research, most research was carried out on explicit and implicit aspects and opinions and was carried out in simple sentences. The purpose of this research is to analyze the sentiment of restaurant reviews using the rule grammar method to extract implicit aspects - explicit opinions in four sentence models, namely simple (Si-AIOE), compound (Co-AIOE), complex (Ce-AIOE), and compound-complex (CoCe-AIOE). The ABSA method is proposed using the development of a grammatical rule extraction method to extract explicit and implicit aspect words and opinion words as the basis for sentence extraction. Rules making is done to take explicit and implicit aspect words and opinion words in Si-AIOE, Co-AIOE, Ce-AIOE, and CoCe-AIOE so that the comparison of the evaluation values can be known. This research uses the Semeval 2015 dataset on Restaurant reviews from the Tripadvisor Website which has been annotated as sentence data for ABSA. The aspect categorization process is then used to categorize aspects into 4 aspect categories, namely Ambience, Food, Service, and Price using hybrid approach. The hybrid approach is combined using Elmo-Wikipedia, grammatical rule extraction, WordNet, TF-ICF, and semantic similarity methods. The results of the aspect extraction obtained value of precision, recall, and f1-measure of 0.80, 0.84, and 0.82, respectively. Meanwhile, the ABSA process uses SentiCircle to classify sentiments into two, namely positive and negative. The results of the ABSA showed that the performance of proposed method achieve for precision, recall, and f1-measure were 0.84, 0.89, and 0.87, respectively.
AB - Sentiment analysis can provide rough recommendations in the form of sentiment from a collection of reviews or can provide recommendations in more detail about sentiment in a particular aspect called aspect-based sentiment analysis (ABSA). Sentiment analysis based on many aspects has been carried out but its accuracy is still being developed. In previous research, most research was carried out on explicit and implicit aspects and opinions and was carried out in simple sentences. The purpose of this research is to analyze the sentiment of restaurant reviews using the rule grammar method to extract implicit aspects - explicit opinions in four sentence models, namely simple (Si-AIOE), compound (Co-AIOE), complex (Ce-AIOE), and compound-complex (CoCe-AIOE). The ABSA method is proposed using the development of a grammatical rule extraction method to extract explicit and implicit aspect words and opinion words as the basis for sentence extraction. Rules making is done to take explicit and implicit aspect words and opinion words in Si-AIOE, Co-AIOE, Ce-AIOE, and CoCe-AIOE so that the comparison of the evaluation values can be known. This research uses the Semeval 2015 dataset on Restaurant reviews from the Tripadvisor Website which has been annotated as sentence data for ABSA. The aspect categorization process is then used to categorize aspects into 4 aspect categories, namely Ambience, Food, Service, and Price using hybrid approach. The hybrid approach is combined using Elmo-Wikipedia, grammatical rule extraction, WordNet, TF-ICF, and semantic similarity methods. The results of the aspect extraction obtained value of precision, recall, and f1-measure of 0.80, 0.84, and 0.82, respectively. Meanwhile, the ABSA process uses SentiCircle to classify sentiments into two, namely positive and negative. The results of the ABSA showed that the performance of proposed method achieve for precision, recall, and f1-measure were 0.84, 0.89, and 0.87, respectively.
KW - Aspect categorization
KW - Aspect-based sentiment analysis
KW - Elmo-wikipedia
KW - Grammatical rule extraction
KW - SentiCircle
KW - TF-ICF
KW - WordNet
UR - http://www.scopus.com/inward/record.url?scp=85114742868&partnerID=8YFLogxK
U2 - 10.22266/ijies2021.1031.17
DO - 10.22266/ijies2021.1031.17
M3 - Article
AN - SCOPUS:85114742868
SN - 2185-310X
VL - 14
SP - 177
EP - 187
JO - International Journal of Intelligent Engineering and Systems
JF - International Journal of Intelligent Engineering and Systems
IS - 5
ER -