Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach

Tho, Cuk; Heryadi, Yaya; Lukas, Lukas; Wibowo, Arief

doi:10.1088/1742-6596/1869/1/012084

Cited by 18 publications

(15 citation statements)

References 5 publications

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“…To illustrate, Handayani et al (2018), Abu Bakar et al (2020), andAbdullah et al (2021) examine one study which included Malay in the application of multilingual sentiment analysis. Our findings connect this work in Malay to more recent work by Tho et al (2021), who looked at Indonesian and Javanese code-mixed sentiment analysis. Indonesian and Malay are closely related (Sneddon, 2003).…”

Section: Collaboration and Connection Between Indonesian And Malay Nl...supporting

confidence: 86%

Scoping natural language processing in Indonesian and Malay for education applications

Maxwelll-Smith¹,

Kohler²,

Suominen³

2022

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Indonesian and Malay are underrepresented in the development of natural language processing (NLP) technologies and available resources are difficult to find. A clear picture of existing work can invigorate and inform how researchers conceptualise worthwhile projects. Using an education sector project to motivate the study, we conducted a wide-ranging overview of Indonesian and Malay human language technologies and corpus work. We charted 657 included studies according to Hirschberg and Manning's 2015 description of NLP, concluding that the field was dominated by exploratory corpus work, machine reading of text gathered from the Internet, and sentiment analysis. In this paper, we identify most published authors and research hubs, and make a number of recommendations to encourage future collaboration and efficiency within NLP in Indonesian and Malay.

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Section: Collaboration and Connection Between Indonesian And Malay Nl...supporting

confidence: 86%

Scoping natural language processing in Indonesian and Malay for education applications

Maxwelll-Smith¹,

Kohler²,

Suominen³

2022

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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“…The results of the Tho's study explain that on the overall performance, Lexicon VADER provides better performance than SentiNetWord (Tho et al, 2021). Another research comparing accuracy using Indonesian lexicon sentiment with SentiWordNet.…”

Section: Review Of Accuracymentioning

confidence: 98%

“…There are several studies that can be considered to reveal the best preprocessing of text. Research from (Tho et al, 2021)aims to compare SentiNetWord and VADER in extracting the polarity of the code-mixed sentences in Indonesian language and Javanese language from twitter. This study using several methods for pre-processing such as removing duplicates, translating to English, filter special characters, transform lower case and filter stop words were conducted on the sentences.…”

Section: A Review Of Preprocessingmentioning

confidence: 99%

“…In research (Tho et al, 2021) positive and negative words are determined from the lexicon model, then calculated using a simple mathematical formula in order to classify the polarity. By comparing with the manual labeling, the result showed that Senti Net Word performed better than VADER in negative sentiments but did not perform well in neutral and positive sentiments.…”

Section: B Review the Labeling Processmentioning

confidence: 99%

“…The review of the lexicon intends to find out the lexicon used in sentiment analysis studies in Indonesian. Some of these studies are from (Tho et al, 2021) whose research aims to compare two lexicon models which are SentiNetWord and VADER in extracting the polarity of the code-mixed sentences in Indonesian language and Javanese language. Both Lexicon are compared and VADER provides higher accuracy.…”

Section: Review Of Lexiconmentioning

confidence: 99%

See 2 more Smart Citations

Lexicon Based Sentiment Analysis in Indonesia Languages : A Systematic Literature Review

Fauziah

Yuwono

Aribowo³

2021

cset

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This systematic literature review aims to determine the trend of lexicon based sentiment analysis research in Indonesian Language in the last two years. The focus of the study is on the understanding of preprocessing used in lexicon-based sentiment analysis studies in the last two years, the lexicon used in these studies, and classification accuracy. The main question in this SLR : what techniques of lexicon based sentiment analysis will provide the highest accuracy. The most widely used preprocessing methods in previous research are tokenization, case conversion, stemming, remove punctuation, remove stop word, remove or replace emoji and emoticons, and normalization or slangword conversion. The sentiment labeling process in previous studies calculated based on the comparison of the number of negative sentiment keywords with positive sentiment keywords in one sentence. The maximum accuracy from previous study is 90%. The most widely used lexicon is NRC and Inset which is a lexicon dictionary in Indonesian. Knowledge of this can be used to propose a better model for lexicon based sentiment analysis in Indonesian Languages.

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Cross-Domain Transfer Learning for Dependency Parsing

Zhou

Zhao

et al. 2019

Natural Language Processing and Chinese Computing

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While structure learning achieves remarkable performance in high-resource languages, the situation differs for under-represented languages due to the scarcity of annotated data. This study focuses on assessing the efficacy of transfer learning in enhancing dependency parsing for Javanese-a language spoken by 80 million individuals but characterized by limited representation in natural language processing. We utilized the Universal Dependencies dataset consisting of dependency treebanks from more than 100 languages, including Javanese. We propose two learning strategies to train the model: transfer learning (TL) and hierarchical transfer learning (HTL). While TL only uses a source language to pre-train the model, the HTL method uses a source language and an intermediate language in the learning process. The results show that our best model uses the HTL method, which improves performance with an increase of 10 % for both UAS and LAS evaluations compared to the baseline model.

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Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach

Cited by 18 publications

References 5 publications

Scoping natural language processing in Indonesian and Malay for education applications

Scoping natural language processing in Indonesian and Malay for education applications

Lexicon Based Sentiment Analysis in Indonesia Languages : A Systematic Literature Review

Cross-Domain Transfer Learning for Dependency Parsing

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