An empirical evaluation of word embedding models for subjectivity analysis tasks

Ritika Nandi, Geetha Maiya, Priya Kamath, Shashank Shekhar

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

Abstract

It is a clearly established fact that good categorization results are heavily dependent on representation techniques. Text representation is a necessity that must be fulfilled before working on any text analysis task since it creates a baseline which even advanced machine learning models fail to compensate. This paper aims to comprehensively analyze and quantitatively evaluate the various models to represent text in order to perform Subjectivity Analysis. We implement a diverse array of models on the Cornell Subjectivity Dataset. It is worth noting that the BERT Language Model gives much better results than any other model but is significantly computationally expensive than the other approaches. We obtained state-of-the-art results on the subjectivity task by fine-tuning the BERT Language Model. This can open up a lot of new avenues and potentially lead to a specialized model inspired by BERT dedicated to subjectivity analysis.

Original languageEnglish
Title of host publicationProceedings of the 2021 1st International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728157900
DOIs
Publication statusPublished - 2021
Event1st IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021 - Bhilai, India
Duration: 19-02-202120-02-2021

Publication series

NameProceedings of the 2021 1st International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021

Conference

Conference1st IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021
Country/TerritoryIndia
CityBhilai
Period19-02-2120-02-21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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