Comparison of Word Embeddings for Sentiment Classification with Preconceived Subjectivity

DOI

10.2991/978-94-6463-094-7_39How to use a DOI?

Keywords

Word2Vec; TF-IDF; BERT; sentiment analysis; word embedding; sentence embedding

Abstract

This research looks into objectivity and subjectivity’s effects on sentiment analysis through word embeddings, namely Word2Vec, Term Frequency-Inverse Document Frequency (TF-IDF), and Bidirectional Encoder Representations from Transformers (BERT). Objectivity corpora are defined as data with a neutral point of view and no biases. In contrast, subjectivity corpora are defined as data from a non-neutral point of view and may contain biases. The goals are to compare the efficacy of numerous embedding methods on sentiment analysis classification after subjectivity analysis. In terms of embedding methods, results from our work show that BERT embedding gives the best outcome for subjectivity classification with an accuracy score of 99.77%. For sentiment classification, TF-IDF provides the highest accuracy with 91.29%.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xi Jie Lee
AU  - Timothy Tzen Vun Yap
AU  - Hu Ng
AU  - Vik Tor Goh
PY  - 2022
DA  - 2022/12/27
TI  - Comparison of Word Embeddings for Sentiment Classification with Preconceived Subjectivity
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 488
EP  - 502
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_39
DO  - 10.2991/978-94-6463-094-7_39
ID  - Lee2022
ER  -