eprintid: 17359
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/73/59
datestamp: 2023-12-19 03:23:45
lastmod: 2023-12-19 03:23:45
status_changed: 2023-12-19 03:07:55
type: article
metadata_visibility: show
creators_name: Tahayna, B.M.A.
creators_name: Ayyasamy, R.K.
creators_name: Akbar, R.
title: Automatic Sentiment Annotation of Idiomatic Expressions for Sentiment Analysis Task
ispublished: pub
keywords: Data mining; Deep learning; Knowledge based systems; Sentiment analysis, Annotation; Bit-error rate; Data augmentation; Data expansion; Deep learning; Fine tuning; Idiomatic expression; Idiomatics; Lexicon; Sentiment analysis; Social networking (online), Bit error rate
note: cited By 4
abstract: Social media users use words and phrases to convey their views or opinions. However, some people use idioms or proverbs that are implicit and indirect to make a stronger impression on the audience or perhaps catch their attention by utilizing funny, sarcastic, or metaphorical phrases. Idioms and proverbs are figurative expressions with a thematically coherent totality that cannot be understood literally. In previous work, the extension of IBM's Sentiment Lexicon of Idiomatic Expressions was proposed to include around 9,000 idioms; a crowdsourcing service manually annotates both lexicons. Therefore, in this research, we provide a knowledge-based expansion approach to avoid human annotation of idioms. For sentiment classification, the proposed method has the advantage that it does not require any fine-tuning for the BERT model. Experimental comparisons show that automated idiom enrichment and annotation are very beneficial for the performance of the sentiment classifier. The expanded annotated lexicon will be made available to the general public. © 2013 IEEE.
date: 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142809399&doi=10.1109%2fACCESS.2022.3222233&partnerID=40&md5=2f46a438255cfc5232a434f1863fb0b5
id_number: 10.1109/ACCESS.2022.3222233
full_text_status: none
publication: IEEE Access
volume: 10
pagerange: 122234-122242
refereed: TRUE
issn: 21693536
citation:   Tahayna, B.M.A. and Ayyasamy, R.K. and Akbar, R.  (2022) Automatic Sentiment Annotation of Idiomatic Expressions for Sentiment Analysis Task.  IEEE Access, 10.  pp. 122234-122242.  ISSN 21693536