Attention Models for Sentiment Analysis Using Objectivity and Subjectivity Word Vectors

Wing Shum Lee, Hu Ng*, Timothy Tzen Vun Yap, Chiung Ching Ho, Vik Tor Goh, Hau Lee Tong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationComputational Science and Technology
EditorsRayner Alfred, Hiroyuki Iida, Haviluddin Haviluddin, Patricia Anthony
PublisherSpringer
Pages51-59
Number of pages9
ISBN (Electronic)9789813340695
ISBN (Print)9789813340688
DOIs
Publication statusPublished - 16 Mar 2021
Event7th International Conference on Computational Science and Technology 2020 - Pattaya, Thailand
Duration: 29 Aug 202030 Aug 2020

Publication series

NameLecture Notes in Electrical Engineering
Volume724
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference7th International Conference on Computational Science and Technology 2020
Abbreviated titleICCST 2020
Country/TerritoryThailand
CityPattaya
Period29/08/2030/08/20

Keywords

  • Objectivity
  • Sentiment analysis
  • Subjectivity
  • Word vectors

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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