Indonesia COVID-19 Online Media News Sentiment Analysis with Lexicon-based Approach and Emotion Detection

Waspodo, Bayu; Nuryasin,; Bany, Amalia Khaerunnisa Nursya; Kusumaningtyas, Rinda Hesti; Rustamaji, Eri

doi:10.1109/citsm56380.2022.9935884

Cited by 3 publications

(1 citation statement)

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“…Each record contains ten binary scores (0 or 1), signaling no association or association with eight primary emotions and documenting positive and negative sentiments for each record. EmoLex was selected because the lexicon began in 2010 and has been used in numerous research projects [20], [21].…”

Section: Lexicon Modelmentioning

confidence: 99%

A Hybrid Method on Emotion Detection for Indonesian Tweets of COVID-19

Purwitasari¹,

Ansyah²,

Kurniawan³

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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As a result of the COVID-19 pandemic, there have been restrictions on activities outside the home which has caused people to interact more and express their emotions through social media platforms, one of which is Twitter. Previous studies on emotion classification used only one feature extraction, namely the lexicon based or word embedding. Feature extraction using the emotion lexicon has the advantage of recognizing emotional words in a sentence while feature extraction using word embedding has the advantage of recognizing the semantic meaning. Therefore, the main contribution to this research is to use two lexicon feature extraction and word embedding to classify emotions. The classification technique used in this research is the Ensemble Voting Classifier by selecting the two best classifiers to try on both types of feature extraction. The experimental results for both types of feature extraction are the same, indicating that the best classifiers are Random Forest and SVM. Models using both types of feature extraction show increased accuracy compared to using only one feature extraction. The results of this emotional analysis can be used to determine the public's reaction to an event, product, or public policy.

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Section: Lexicon Modelmentioning

confidence: 99%