Abstract
Sentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Unlike the methods that lexical encode or add information to the corpus, this method adds presentation of raw data based on the expert’s knowledge in the ontology. Once the data has a rich knowledge of the topic, the efficiency of the machine learning algorithms is significantly enhanced. Thus, this method is appliable to embed knowledge in datasets in other languages. The test results show that deep learning methods achieved considerably higher accuracy when trained with the KPRO method’s dataset than when trained with datasets not processed by this method. Therefore, this method is a novel approach to improve the accuracy of deep learning algorithms and increase the reliability of new datasets, thus making them ready for mining.
Highlights
Sentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets
This will be the basis for deep learning methods to understand language, thereby improving deep learning methods used for sentiment classification and other problems
With the set of aspect-level sentences 2 (SAS2) dataset, the BERT-base method achieved an accuracy of 90.36%, which is equivalent to other deep learning methods
Summary
Sentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Gu et al.[19] performed sentiment classification using the Amazon dataset with two polarities using a CNN and word embeddings created using word2vec and achieved an accuracy of 84.87%. Zhai et al.[23] encountered a similar situation when proposing a model combining many LSTM modules to conduct sentiment analysis for Course, Education, and Restaurant datasets. This model achieved an accuracy of 94.6% on the Education dataset. It scored less well on the other two datasets, achieving an accuracy of 81.4% on the Course dataset and 79.6% on the Restaurant dataset
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