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Published in:
2021 | OriginalPaper | Chapter
Attention Models for Sentiment Analysis Using Objectivity and Subjectivity Word Vectors
Authors : Wing Shum Lee, Hu Ng, Timothy Tzen Vun Yap, Chiung Ching Ho, Vik Tor Goh, Hau Lee Tong
Published in: Computational Science and Technology
Publisher: Springer Singapore
Abstract
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.
