Sentiment analysis of Covid-19 vaccine tweets utilizing Naïve Bayes

Abdurrahim, Abdurrahim; Syafaah, Lailis; Lestandy, Merinda

doi:10.1063/5.0094607

Cited by 3 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data training digunakan untuk melatih model klasifikasi sedangkan data testing digunakan untuk melakukan evaluasi model [5]. Perhitungan probabilitas dalam metode Gaussian naïve bayes dilakukan dengan menggunakan rumus yang ditunjukkan pada Persamaan (1) dan Persamaan (2) [15].…”

Section: Klasifikasi Gaussian Naïve Bayesunclassified

Implementasi Analisis Sentimen Masyarakat Mengenai Kenaikan Harga BBM Pada Komentar Youtube Dengan Metode Gaussian naïve bayes

2022

View full text Add to dashboard Cite

Youtube merupakan platform video terbesar di dunia dengan total pengguna sebanyak 1,5 miliar pada tahun 2018. Youtube menjadi salah satu platform penyedia informasi, salah satunya yakni kenaikan harga minyak mentah dunia hingga berada di atas US$100 per barel. Berdasarkan permasalahan tersebut, penulis melakukan penelitian terkait analisis sentimen dari komentar pengguna Youtube mengenai kenaikan harga BBM menggunakan metode Gaussian naïve bayes. Percobaan dilakukan menggunakan 3053 dataset dengan pelabelan menggunakan lexicon dan split data 8:2. Penerapan vektorisasi kata menggunakan word embedding Fasttext dan Bag of word sebagai pembanding terhadap akurasi. Percobaan dilakukan dengan kombinasi perbedaan dimensi size pada proses pembuatan language model fasttext. Berdasarkan hasil penelitian yang telah dilakukan, didapatkan nilai akurasi tertinggi pada percobaan dengan dataset tanpa filtering stopword dan model fasttext size 100 dengan akurasi sebesar 74%. Berdasarkan hasil evaluasi, sistem yang dibangun dapat mengklasifikasikan sentimen atau opini publik ke dalam sentimen positif dan sentiment negatif secara otomatis.

show abstract

Section: Klasifikasi Gaussian Naïve Bayesunclassified

Implementasi Analisis Sentimen Masyarakat Mengenai Kenaikan Harga BBM Pada Komentar Youtube Dengan Metode Gaussian naïve bayes

2022

View full text Add to dashboard Cite

show abstract

“…Studies that analyse Twitter data for sentiment analysis apply a variety of different algorithms such as Naive Bayes, Support Vector Machines and Gradient Boosting Decision Trees 11 12. Classification algorithms have been used to understand the opioid crisis11, COVID-19 pandemic13 and the HIV epidemic14 NLP algorithms improve the accuracy and efficacy of analysing language in large volumes 15. These methods may allow researchers to gain insights on disease symptoms and make predictions on potential outbreaks in a timely fashion 3 15 16.…”

Section: Introductionmentioning

confidence: 99%

Addressing bias in artificial intelligence for public health surveillance

2023

View full text Add to dashboard Cite

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been implemented to analyse large volumes of text from social media platforms to gain insights on disease symptoms, understand barriers to care and predict disease outbreaks. However, AI-based decisions may contain biases that could misrepresent populations, skew results or lead to errors. Bias, within the scope of this paper, is described as the difference between the predictive values and true values within the modelling of an algorithm. Bias within algorithms may lead to inaccurate healthcare outcomes and exacerbate health disparities when results derived from these biased algorithms are applied to health interventions. Researchers who implement these algorithms must consider when and how bias may arise. This paper explores algorithmic biases as a result of data collection, labelling and modelling of NLP algorithms. Researchers have a role in ensuring that efforts towards combating bias are enforced, especially when drawing health conclusions derived from social media posts that are linguistically diverse. Through the implementation of open collaboration, auditing processes and the development of guidelines, researchers may be able to reduce bias and improve NLP algorithms that improve health surveillance.

show abstract