research-article

Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis

Authors:

Rini Wijayanti,

Andria ArisalAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 20, Issue 1

Article No.: 15, Pages 1 - 16

https://doi.org/10.1145/3425632

Published: 02 March 2021 Publication History

Abstract

A novel Indonesian sentiment lexicon (SentIL -- Sentiment Indonesian Lexicon) is created with an automatic pipeline; from creating sentiment seed words, adding new words with slang words, emoticons, and from the given dictionary and sentiment corpus, until tuning sentiment value with tagged sentiment corpus. It begins by taking seed words from WordNet Bahasa that mapped with sentiment value from English SentiWordNet. The seed words are enriched by combining the dictionary-based method with words’ synonyms and antonyms, and corpus-based methods with word embedding for word similarity that trained in positive and negative sentiment corpus from online marketplaces review and Twitter data. The valence score of each lexicon is recalculated based on its relative occurrence in the corpus. We also add some famous slang words and emoticons to enrich the lexicon. Our experiment shows that the proposed method can provide an increase of 3.5 times lexicon number as well as improve the accuracy of 80.9% for online review and 95.7% for Twitter data, and they are better than other published and available Indonesian sentiment lexicons.

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Cited By

Yin XChen L(2023)Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention InteractionInternational Journal of Data Warehousing and Mining10.4018/IJDWM.33385419:1(1-20)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.4018/IJDWM.333854
Luo HSong WZhou WLin XYu S(2023)An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated ContentSustainability10.3390/su15181333615:18(13336)Online publication date: 6-Sep-2023
https://doi.org/10.3390/su151813336
Vijay Verma P(2023)Framework to Create Sentiment Lexicons For Extremism Detection in Text Documents2023 International Conference on IoT, Communication and Automation Technology (ICICAT)10.1109/ICICAT57735.2023.10263761(1-6)Online publication date: 23-Jun-2023
https://doi.org/10.1109/ICICAT57735.2023.10263761
Show More Cited By

Index Terms

Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis
1. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Decision support systems
      1. Data analytics

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 20, Issue 1

Special issue on Deep Learning for Low-Resource Natural Language Processing, Part 1 and Regular Papers

January 2021

332 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3439335

Editor:
Imed Zitouni
Google, USA

Issue’s Table of Contents

Copyright © 2021 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 March 2021

Accepted: 01 September 2020

Revised: 01 June 2020

Received: 01 July 2019

Published in TALLIP Volume 20, Issue 1

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Cited By

Yin XChen L(2023)Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention InteractionInternational Journal of Data Warehousing and Mining10.4018/IJDWM.33385419:1(1-20)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.4018/IJDWM.333854
Luo HSong WZhou WLin XYu S(2023)An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated ContentSustainability10.3390/su15181333615:18(13336)Online publication date: 6-Sep-2023
https://doi.org/10.3390/su151813336
Vijay Verma P(2023)Framework to Create Sentiment Lexicons For Extremism Detection in Text Documents2023 International Conference on IoT, Communication and Automation Technology (ICICAT)10.1109/ICICAT57735.2023.10263761(1-6)Online publication date: 23-Jun-2023
https://doi.org/10.1109/ICICAT57735.2023.10263761
Dhammananda JBudi ISantoso A(2023)Sentiment Analysis and Topic Modeling of E-Grocery Application Reviews Using Naive Bayes and Support Vector Machine: A Case Study of Segari Data Review on the Google Play Store2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)10.1109/ICE3IS59323.2023.10335206(13-18)Online publication date: 9-Aug-2023
https://doi.org/10.1109/ICE3IS59323.2023.10335206
Kroon AWelbers KTrilling Dvan Atteveldt W(2023)Advancing Automated Content Analysis for a New Era of Media Effects Research: The Key Role of Transfer LearningCommunication Methods and Measures10.1080/19312458.2023.226137218:2(142-162)Online publication date: 4-Oct-2023
https://doi.org/10.1080/19312458.2023.2261372
Bensoltane RZaki T(2023)Aspect-based sentiment analysis: an overview in the use of Arabic languageArtificial Intelligence Review10.1007/s10462-022-10215-356:3(2325-2363)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10462-022-10215-3
Sultan SJavaid QRehman EAziz Alahmadi AUllah NKhan W(2022)MELex: The Construction of Malay-English Sentiment LexiconComputers, Materials & Continua10.32604/cmc.2022.02113171:1(1789-1805)Online publication date: 2022
https://doi.org/10.32604/cmc.2022.021131
Zha MHu CShi Y(2022)Sentiment lexicon construction for Chinese book reviews based on ultrashort reviewsThe Electronic Library10.1108/EL-07-2021-014740:3(221-236)Online publication date: 12-Apr-2022
https://doi.org/10.1108/EL-07-2021-0147
Zhang WLi LZhu YYu PWen J(2022)CNN-LSTM neural network model for fine-grained negative emotion computing in emergenciesAlexandria Engineering Journal10.1016/j.aej.2021.12.02261:9(6755-6767)Online publication date: Sep-2022
https://doi.org/10.1016/j.aej.2021.12.022

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