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Articles
Published: 25-09-2021

Sentiment analysis and modeling of the Covid-19 pandemic topic on Twitter social media using the Naïve Bayes Classifier and Latent Dirichlet Allocation.

Universitas Perwira Purbalingga
Fakultas Sains dan Teknik Universitas Perwira
Fakultas Sains dan Teknik Universitas Perwira
Topic Modeling, Latent Semantic Indexing, Sentiment Analysis, Naïve Bayes, Covid-19

Abstract

\The Corona virus or Covid-19 is of particular concern around the world. Many people talk about this virus through posting comments and opinions on Social Media. Twitter is one of the social media that is currently still widely used by the public to convey opinions in the form of a collection of words called tweets. Tweets related to the topic of Covid-19 can be classified using the Topic Modeling method to produce a data topic that is often discussed by Twitter users. One of the algorithms used to perform Topic Modeling is using Latent Dirichleat Allocation(LDA). In this study, LDA was used to find out what words appeared in the tweets about Covid-19 that the public had uploaded via Twitter. Before the tweet data is modeled with LDA, sentiment analysis is carried out first with the Naïve Bayes Classifier to produce Positive, Negative and Neutral sentiments.

 

 

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How to Cite

Prakosa, Herjuna Ardi, Ari Budi, and Siti Nasiroh. 2021. “Sentiment Analysis and Modeling of the Covid-19 Pandemic Topic on Twitter Social Media Using the Naïve Bayes Classifier and Latent Dirichlet Allocation”. JNANALOKA 2 (2):73-78. https://doi.org/10.36802/jnanaloka.2021.v2-no2-73-78.