An Ontology-based Sentiment Analysis Approach To Discovering Hidden Affected Objects

Phương pháp phân tích quan điểm dựa trên Ontology để khám phá các đối tượng ẩn

  • Anh Phuong Le
  • Minh Duc Nguyen
  • Dinh Hoa Cuong Nguyen
  • THI HUONG GIANG NGUYEN Department of Computer Science, University of Education, Hue University
Keywords: Sentiment analysis, ontology, long short-term memory, semantic reasoning, hidden affected object


Nowadays, the topic of sentiment analysis has attracted a wide range of artificial intelligence technologies such
as Natural Language Processing (NLP), neural network. Long Short-Term Memory (LSTM), a deep neural network, has proven the effiency in the task of sentiment classification. However, there is a shortage of semantic knowledge from such classification results. This paper introduces a sentiment analysis approach based on a knowledge model to discover hidden affected objects, which combines ONtology and SentiMent technology, named ONSEM. In order to confirm the efficiency of the ONSEM framework, an experiment on a sentiment analysis dataset was developed and then yielded promising experimental results.


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