Attention Models for Sentiment Analysis Using Objectivity and Subjectivity Word Vectors


Lee, Wing Shum and Ng, Hu and Yap, Timothy Tzen Vun and Ho, Chiung Ching and Goh, Vik Tor and Tong, Hau Lee (2021) Attention Models for Sentiment Analysis Using Objectivity and Subjectivity Word Vectors. In: 7th International Conference on Computational Science and Technology, ICCST 2020, 29 - 30 August 2020, Pattaya, Thailand.

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In this research, we look at the notions of objectivity and subjectivity and create word embeddings from them for the purpose of sentiment analysis. We created word vectors from two datasets, the Wikipedia English Dataset for objectivity and the Amazon Product Reviews Data dataset for subjectivity. A model incorporating an Attention Mechanism was proposed. The proposed Attention model was compared to Logistic Regression, Linear Support Vector Classification models, and the former was able to achieve the highest accuracy with large enough data through augmentation. In the case of objectivity and subjectivity, models trained with the objectivity word embeddings performed worse than their counterpart. However, when compared to the BERT model, a model also with Attention Mechanism but has its own word embedding technique, the BERT model achieved higher accuracy even though model training was performed with only transfer learning.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Sentiment Analysis, Data mining
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Faculty of Engineering (FOE)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 01 May 2021 14:14
Last Modified: 12 Apr 2023 07:53


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