TY - JOUR
T1 - Aspect based sentiment analysis for restaurant reviews using hybrid ELMo-wikipedia and hybrid expanded opinion lexicon-senticircle
AU - Nurifan, Farza
AU - Sarno, Riyanarto
AU - Sungkono, Kelly Rossa
N1 - Publisher Copyright:
© 2019 Intelligent Network and Systems Society.
PY - 2019
Y1 - 2019
N2 - Many restaurant review analysis have been done, however only few analysis have been done for specific aspects of a restaurant. In this context this paper proposes aspect based restaurant analysis which includes Physical environment, Food quality, Service quality and Price fairness. The analysis steps include Aspect Term Extraction (ATE), Aspect Keyword Extraction (AKE), Aspect Categorization (AC) and Sentiment Analysis (SA). ATE employs the modification of Double Propagation method and several Topic Modelling methods, AKE utilizes Term Frequency-Inverse Cluster Frequency (TF-ICF), in AC we propose Hybrid ELMo-Wikipedia (HEW), and in SA we propose Hybrid Expanded Opinion Lexicon-SentiCircle (HEOLS). The results show that our modification of the methods used in ATE could increase the f1measure of the AC by average 2%, then the HEW that we proposed had better f1measure compared to other similar methods by average 6%. Other than that, our proposed HEOLS can expand and redetermine the Opinion Lexicon polarity and can increase f1measure of SA by 6%.
AB - Many restaurant review analysis have been done, however only few analysis have been done for specific aspects of a restaurant. In this context this paper proposes aspect based restaurant analysis which includes Physical environment, Food quality, Service quality and Price fairness. The analysis steps include Aspect Term Extraction (ATE), Aspect Keyword Extraction (AKE), Aspect Categorization (AC) and Sentiment Analysis (SA). ATE employs the modification of Double Propagation method and several Topic Modelling methods, AKE utilizes Term Frequency-Inverse Cluster Frequency (TF-ICF), in AC we propose Hybrid ELMo-Wikipedia (HEW), and in SA we propose Hybrid Expanded Opinion Lexicon-SentiCircle (HEOLS). The results show that our modification of the methods used in ATE could increase the f1measure of the AC by average 2%, then the HEW that we proposed had better f1measure compared to other similar methods by average 6%. Other than that, our proposed HEOLS can expand and redetermine the Opinion Lexicon polarity and can increase f1measure of SA by 6%.
KW - Aspect based sentiment analysis
KW - Elmo
KW - Natural language processing
KW - Opinion mining
KW - SentiCircle
KW - Sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85077893742&partnerID=8YFLogxK
U2 - 10.22266/ijies2019.1231.05
DO - 10.22266/ijies2019.1231.05
M3 - Article
AN - SCOPUS:85077893742
SN - 2185-310X
VL - 12
SP - 47
EP - 58
JO - International Journal of Intelligent Engineering and Systems
JF - International Journal of Intelligent Engineering and Systems
IS - 6
ER -