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
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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