Analysis of Marketplace Social Media User Engagement by Topic

Pribadi, Muhammad Rizky; Widiyanto, Eka Puji; Hermanto, Dedy; Ricoida, Desy Iba; Pibriana, Desi; Rusbandi,

doi:10.1109/icitisee57756.2022.10057722

Cited by 2 publications

(1 citation statement)

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“…The Latent Dirichlet Allocation (LDA) algorithm can be applied to various types of text data, such as news articles, business documents, and even text messages or social media. The Latent Dirichlet Allocation (LDA) algorithm is widely used in document grouping or classification applications, sentiment analysis, and natural language processing, so researchers chose the Latent Dirichlet Allocation (LDA) algorithm as the model for this research [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis Engagement of Instagram Visitors at University of Multi Data Palembang Based on Topic Using Lda

Jerin,

Rizky Pribadi,

Rusbandi

2023

JRSIT

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Social media has a big impact on everyday life, one of which is to communicate or to get information. Therefore, the development of social media applications makes people use social media applications to find information via the internet. The Instagram applications is one of the most popular social media because it has different topics based on post in the from of images or videos. Therefore, it is very difficult to identify a topic manually. One way to get implied information on social media is through topic modeling. This research was conducted to analyze the application of the LDA method to identify what topics are on Instagram at Multi Data Palembang University. The topics chosen in this study were obtained from LDA based on coherence values. This research uses 2 models, namely random forest and decision tree. Each model tested will produce different accuracy, precision, recall, and f1-score values. Tests were carried out on the LDA labeling dataset and manual labeling, the test results on the LDA labeling dataset were very good using the random forest model with an accuracy values of 78%, precision 80%, recall 66.66%, and f1-score 72.72%.

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Section: Introductionmentioning

confidence: 99%