ONTOLOGY-BASED APPROACH FOR FEATURE LEVEL SENTIMENT ANALYSIS
aboelela, E., Gad, W., Ismail, R. (2021). ONTOLOGY-BASED APPROACH FOR FEATURE LEVEL SENTIMENT ANALYSIS. EKB Journal Management System, 21(3), 1-12. doi: 10.21608/ijicis.2021.77345.1094
eman mahmoud aboelela; Walaa Gad; Rasha Ismail. "ONTOLOGY-BASED APPROACH FOR FEATURE LEVEL SENTIMENT ANALYSIS". EKB Journal Management System, 21, 3, 2021, 1-12. doi: 10.21608/ijicis.2021.77345.1094
aboelela, E., Gad, W., Ismail, R. (2021). 'ONTOLOGY-BASED APPROACH FOR FEATURE LEVEL SENTIMENT ANALYSIS', EKB Journal Management System, 21(3), pp. 1-12. doi: 10.21608/ijicis.2021.77345.1094
aboelela, E., Gad, W., Ismail, R. ONTOLOGY-BASED APPROACH FOR FEATURE LEVEL SENTIMENT ANALYSIS. EKB Journal Management System, 2021; 21(3): 1-12. doi: 10.21608/ijicis.2021.77345.1094

ONTOLOGY-BASED APPROACH FOR FEATURE LEVEL SENTIMENT ANALYSIS

International Journal of Intelligent Computing and Information Sciences
Article 10, Volume 21, Issue 3, November 2021, Page 1-12  XML PDF (1.05 MB)
Document Type: Original Article
DOI: 10.21608/ijicis.2021.77345.1094
View on SCiNiTO View on SCiNiTO
Authors
eman mahmoud aboelela email orcid 1; Walaa Gadorcid 1; Rasha Ismailorcid 2
1Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
2Vice Dean for Postgraduate Studies & Research, Faculty of Computer and Information Sciences, Ain Shams University
Abstract
Through state-of-the-art digitalization, we can see a massive growth in user generated content on the web that provides feedback from people on a variety of topics. However, manually managing large-scale user feedback would be a difficult task and a waste of time. Therefore, the concept of sentiment analysis is emerged. Sentiment analysis is a computerized study of individuals' feelings and opinions about an entity or product. It can be executed at three different levels: document level, sentence or phrase level, and feature level. This paper proposes a novel ontology-based approach for feature level sentiment analysis. The proposed approach extracts the product features using semantic similarity and Wordnet ontology and uses the SentiWordent dictionary to classify the users’ comments as positive and negative. Furthermore, it manages negative words to obtain more precise classification results. The proposed approach is assessed by using two benchmark amazon products’ datasets in terms of accuracy; recall, precision, and f-measure. The performance reaches to 92.4% accuracy, 97.2% precision, 92.8 % recall, and 94.4% f-measure.
Keywords
Sentiment Analysis; Wordnet Ontology; SentiWordnet; Semantic Similarity
Statistics
Article View: 226
PDF Download: 339