Sentiment Analysis of COVID-19 Vaccines in Indonesia on Twitter Using Pre-Trained and Self-Training Word Embeddings

Agustiningsih, Kartikasari Kusuma; Utami, Ema; Alsyaibani, Muhammad Altoumi

doi:10.21609/jiki.v15i1.1044

Cited by 7 publications

(5 citation statements)

References 22 publications

(42 reference statements)

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“… 31 While Agustiningsih et al captured tweets within September 2021. 32 Both used different method of learning framework. Sumertajaya et al implemented support vector machine (SVM) and random forest, while Agustiningsih et al employed bidirectional Long Short-Term Memory (LSTM) combined with word embedding.…”

Section: Resultsmentioning

confidence: 99%

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Sentiment analysis of Indonesian tweets on COVID-19 and COVID-19 vaccinations

Kalanjati,

Hasanatuludhhiyah,

d'Arqom

et al. 2024

F1000Res

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Background Sentiments and opinions regarding COVID-19 and the COVID-19 vaccination on Indonesian-language Twitter are scarcely reported in one comprehensive study, and thus were aimed at our study. We also analyzed fake news and facts, and Twitter engagement to understand people’s perceptions and beliefs that determine public health literacy. Methods We collected 3,489,367 tweets data from January 2020 to August 2021. We analyzed factual and fake news using the string comparison method. The difflib library was used to measure similarity. The user’s engagement was analyzed by averaging the engagement metrics of tweets, retweets, favorites, replies, and posts shared with sentiments and opinions regarding COVID-19 and COVID-19 vaccination. Result Positive sentiments on COVID-19 and COVID-19 vaccination dominated, however, the negative sentiments increased during the beginning of the implementation of restrictions on community activities (PPKM). The tweets were dominated by the importance of health protocols (washing hands, keeping distance, and wearing masks). Several types of vaccines were on top of the word count in the vaccine subtopic. Acceptance of the vaccination increased during the studied period, and the fake news was overweighed by the facts. The tweets were dynamic and showed that the engaged topics were changed from the nature of COVID-19 to the vaccination and virus mutation which peaked in the early and middle terms of 2021. The public sentiment and engagement were shifted from hesitancy to anxiety towards the safety and effectiveness of the vaccines, whilst changed again into wariness on an uprising of the delta variant. Conclusion Understanding public sentiment and opinion can help policymakers to plan the best strategy to cope with the pandemic. Positive sentiments and fact-based opinions on COVID-19, and COVID-19 vaccination had been shown predominantly. However, sufficient health literacy levels could yet be predicted and sought for further study.

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Section: Resultsmentioning

confidence: 99%

“…Sumertajaya et al implemented support vector machine (SVM) and random forest, while Agustiningsih et al employed bidirectional Long Short-Term Memory (LSTM) combined with word embedding. 31 , 32 …”

Section: Resultsmentioning

confidence: 99%

Sentiment analysis of Indonesian tweets on COVID-19 and COVID-19 vaccinations

Kalanjati,

Hasanatuludhhiyah,

d'Arqom

et al. 2024

F1000Res

View full text Add to dashboard Cite

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“…Penelitian oleh Kartikasari Kusuma Agustiningsih, Ema Utami, Omar Muhammad Altoumi Alsyaibani yang berjudul Sentiment Analysis of COVID-19 Vaccines in Indonesia on Twitter Using Pre-Trained and Self-Training Word Embeddings mendapat akurasi tertinggi dari grup skenario GloVe yaitu 92.55% yang dihasilkan oleh model menggunakan selftrained GloVe dan dilatih pada dataset unstemmed. Disisi lain pada grup skenario fastText yaitu 92.33% yang dihasilkan oleh model menggunakan self-trained fastText dan dilatih pada dataset stemmed [8].…”

Section: Tinjauan Pustakaunclassified

Analisis Sentimen Terhadap Kenaikan Harga Bahan Bakar Minyak Menggunakan Long Short-Term Memory

Ikhsan

2023

IJCSR

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Pada tahun 2022 terjadi kenaikan harga bahan bakar minyak dunia. Dengan naiknya harga bahan bakar minyak ini secara tidak langsung akan berpengaruh terhadap harga material, inflasi, dan biaya hidup. Kenaikan harga bahan bakar minyak menjadi kontroversi hingga terjadi demo di berbagai wilayah. Kebijakan dinaikan harga bahan bakar minyak ini mendapat berbagai respons dari pengguna Twitter. Penelitian ini bertujuan untuk mengetahui akurasi prediksi dan pola sentimen model LSTM (Long Short-Term Memory) dari opini pengguna Twitter terhadap topik kenaikan harga bahan bakar minyak pada bulan Juli 2022. Data yang digunakan adalah data berbahasa English dengan dua kelas sentimen yaitu negative dan positive. Model LSTM dilakukan percobaan sebanyak lima kali. Hasil penelitian ini menunjukan bahwa akurasi tertinggi terdapat pada percobaan ke-1 dan ke-3 yaitu 90% dan pola sentimen cenderung positif.

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“…The LSTM method is well suited to classify, examine, and forecast time series of ambiguous duration. (Agustiningsih et al, 2022)(Dicky Wahyu Hariyanto, 2020.…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

LSTM and Bidirectional GRU Comparison for Text Classification

Asrawi,

Utami,

Yaqin

2023

SinkrOn

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Although the phrases machine learning and AI are frequently used interchangeably and are frequently discussed together, they do not have the same meanings. While all artificial intelligence (AI) is machine learning, not all AI is machine learning, which is a key distinction. In the beginning, machine learning and natural language processing (NLP) are related since machine learning is frequently employed as a tool for NLP tasks. The advantage of NLP is that it can perform analysis, and examine a lot of data, including comments on social media accounts and hundreds of online customer evaluations. Text classification is essentially what needs to be done. This study compares Bidirectional GRU and LSTM as text classification algorithms using 20,000 newsgroup documents from 20 newsgroups from The UCI KDD Archive. After using the suggested model, we compare it to the long short-term memory and bidirectional GRU models for accuracy and validation. The results of the two comparisons show that the bidirectional GRU model performs better than the long short-term memory model. And this is a successful classification of text using a deep learning algorithm that uses a bidirectional GRU.

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Sentiment Analysis of COVID-19 Vaccines in Indonesia on Twitter Using Pre-Trained and Self-Training Word Embeddings

Cited by 7 publications

References 22 publications

Sentiment analysis of Indonesian tweets on COVID-19 and COVID-19 vaccinations

Sentiment analysis of Indonesian tweets on COVID-19 and COVID-19 vaccinations

Analisis Sentimen Terhadap Kenaikan Harga Bahan Bakar Minyak Menggunakan Long Short-Term Memory

LSTM and Bidirectional GRU Comparison for Text Classification

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