Sentiment analysis and topic modeling of BPJS Kesehatan based on twitter crawling data using Indonesian Sentiment Lexicon and Latent Dirichlet Allocation algorithm

Dikiyanti, T D; Rukmi, Alvida Mustika; Irawan, Mohammad Isa

doi:10.1088/1742-6596/1821/1/012054

Cited by 15 publications

(14 citation statements)

References 3 publications

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“…Twitter data crawling is a process of extracting user data and tweets originating from Twitter based on specific keywords [7]. Data extraction is performed by implementing Application Programming Integration (API) [8].…”

Section: Data Collectionmentioning

confidence: 99%

Aspect-Bas1ed Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion

Alhakiem¹,

Setiawan²

2022

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

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Social media has recently been widely used by users, especially Indonesians, as a place to express themselves in sentences, pictures, sounds, or videos. Twitter is one of the social media favored by people of diverse ages. Twitter is a social media that provides features like social media in general. However, Twitter has a unique feature where users can send or read text messages limited to only a few characters. Therefore, user tweets with topics related to a particular product can be utilized by companies to become input in the development of these products. This research was conducted using tweet data on the topic of Telkomsel, which is divided into two aspects, namely signal and service. Aspect-based sentiment analysis of Telkomsel was carried out using Logistic Regression with FastText feature expansion to reduce vocabulary mismatch in tweets so that the classification stage can be performed optimally. In addition, the Synthetic Minority Oversampling Technique (SMOTE) sampling method was applied to overcome data imbalance. The test results prove that feature expansion can improve F1-Score values for signal and service aspects. For the signal aspect, F1-Score increased by 3.33% from the baseline with a value of 96.48%. While for the service aspect, F1-Score increased by 12.91% from the baseline with a value of 95.57%.

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Section: Data Collectionmentioning

confidence: 99%

Aspect-Bas1ed Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion

Alhakiem¹,

Setiawan²

2022

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

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“…Analisis sentimen terhadap BPJS Kesehatan juga pernah dilakukan oleh Dikiyanti dkk. [4] baru-baru ini berdasarkan data yang diperoleh dari Twitter. Rintyarna [5] telah melakukan analisis sentimen terhadap pandangan masyarakat Indonesia terhadap makanan organik selama pandemic COVID-19.…”

Section: Pendahuluanunclassified

Analisis Sentimen Menggunakan Arsitektur Long Short-Term Memory (Lstm) Terhadap Fenomena Citayam Fashion Week

Farsiah

Misbullah²,

Husaini³

2022

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Sentiment analysis of the text aims to recognize whether a text contains positive, negative, or neutral emotions. The results of the analysis can be used as a tool for making decisions on an issue. Recently, the Citayam Fashion Week event become an issue that is extremely debated in Indonesia, especially in July 2022 on social media. The issue has motivated us to do sentiment analysis for better making decisions. In our work, the dataset is collected from Indonesian people's tweets with the keywords Citayam Fashion Week. Furthermore, each tweet will be labeled with a positive, negative, or neutral class based on the Indonesian lexical. This research produces a model based on Long Short Term Memory (LSTM) structure to predict every Indonesian tweet into the category of positive, negative, or neutral sentiment related to public views and opinions about the Citayam Fashion Week phenomenon. The model accuracy shows that the LSTM obtained good performance which is 88%.

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“…Crawling adalah teknik yang digunakan untuk mengumpulkan data yang terdapat dalam suatu web. Di mana akan diperoleh informasi sesuai dengan kata kunci yang dicari dengan bantuan Twitter API (Dikiyanti, Rukmi and Irawan, 2021).…”

Section: Crawling Dataunclassified

Analisis Sentimen Pelaksanaan Vaksinasi Covid-19 secara Massal pada Media Sosial Twitter

Nuraini¹,

Pertiwi²,

Subarkah³

et al. 2022

semnasoffstat

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Virus corona atau Covid-19 menyerang hampir seluruh negara di dunia, begitu juga Indonesia. Pemerintah Indonesia melaksanakan beberapa kebijakan untuk menangani penyebaran virus Covid-19 di masyarakat yaitu dengan vaksinasi massal. Pelaksanaan vaksinasi massal menjadi pembicaraan masyarakat salah satunya di media sosial Twitter. Penelitian ini bertujuan untuk menganalisis sentimen pelaksanaan vaksinasi massal di Indonesia menggunakan perbandingan antara klasifikasi Naïve Bayes, Random Forest, dan Support Vector Machine (SVM). Hasil penelitian menunjukan bahwa penggunaan metode klasifikasi SVM menghasilkan F1-Score Weighted Average lebih tinggi daripada metode lain yaitu sebesar 84%. Selain itu, dapat disimpulkan bahwa sebagian besar masyarakat pro terhadap pelaksanaan vaksin massal. Sehingga metode SVM dapat digunakan pemerintah untuk mengklasifikasikan sentimen masyarakat terhadap pengadaan vaksinasi massal selanjutnya dan dijadikan dasar pemerintah untuk mempertahankan program vaksinasi massal ini sebagai upaya mencegah penyebaran Covid-19.

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Sentiment analysis and topic modeling of BPJS Kesehatan based on twitter crawling data using Indonesian Sentiment Lexicon and Latent Dirichlet Allocation algorithm

Cited by 15 publications

References 3 publications

Aspect-Bas1ed Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion

Aspect-Bas1ed Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion

Analisis Sentimen Menggunakan Arsitektur Long Short-Term Memory (Lstm) Terhadap Fenomena Citayam Fashion Week

Analisis Sentimen Pelaksanaan Vaksinasi Covid-19 secara Massal pada Media Sosial Twitter

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