scholarly journals Sentiment Mining from Online Patient Experience using Latent Dirichlet Allocation Method

2016 ◽  
Vol 9 (19) ◽  
Author(s):  
P. Padmavathy ◽  
A. Anny Leema
Keyword(s):  
Patient Experience ◽  
Latent Dirichlet Allocation ◽  
Allocation Method ◽  
Sentiment Mining ◽  
Dirichlet Allocation

Analisis topik konten channel YouTube K-pop Indonesia menggunakan Latent Dirichlet Allocation

Teknologi ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 16-25
Author(s):  
Alfrida Rahmawati ◽  
◽  
Najla Lailin Nikmah ◽  
Reynaldi Drajat Ageng Perwira ◽  
Nur Aini Rakhmawati ◽  
...  
Keyword(s):  
Text Mining ◽  
New Media ◽  
Digital Technology ◽  
Latent Dirichlet Allocation ◽  
Optimal Number ◽  
Allocation Method ◽  
Internet Users ◽  
The World ◽  
Dirichlet Allocation

The development of digital technology has brought new media, one of which is Youtube, which is now one of the most widely used applications for internet users in the world. The growth of the audience which is known as viewers, is also suported by the contribution from the content creators or also known as YouTubers from Indonesian. The more the viewers grow, the more their demand for trend content are also grwoing at surprisingly speed in one of the topics which is H-pop. In this study, the author wanted to see the dominant topics that K-pop YouTubers often upload to support content creator. This research was conducted using the Latent Dirichlet Allocation method. The analysis was carried out on after using text mining on 2563 videos from 10 K-pop YouTuber accounts with more than 100,000 subscribers. To determine the optimal number of topics by looking at the value of perplexity and topic conherence. The results obtained are the top 5 topics that are the content material in the uploaded video. These topics include reactions to dance covers, unboxing on albums and conducting reviews, riddles from K-pop dances and vlogs together to discuss about covers and reactions to sounds on K-pop songs.

scholarly journals Topic Modeling for Twitter Users Regarding the "Ruanggguru" Application

2020 ◽  
Vol 21 (2) ◽  
pp. 149
Author(s):  
Bagus Wicaksono Arianto ◽  
Gangga Anuraga
Keyword(s):  
Topic Modeling ◽  
Public Perception ◽  
Latent Dirichlet Allocation ◽  
The Public ◽  
Allocation Method ◽  
Twitter Account ◽  
Twitter Users ◽  
A Company ◽  
Dirichlet Allocation ◽  
Expansion Strategies

PT Ruang Raya Indonesia ("Ruangguru") is the largest and most comprehensive technology company in Indonesia that focuses on education-based services. In 2019 there were 15 million Ruangguru users and 300.00 teachers who had joined and were present in 32 provinces in Indonesia. It prepared a number of expansion strategies to become a company valued at more than US $ 1 billion in the next year or two. The purpose of this research is to classify the opinions of Ruangguru users about the services provided so that it can be an evaluation material in improving their services using the latent direchlet allocation method. The data used comes from a collection of tweets of Twitter users in Indonesia using the Twitter API. The Twitter account used in this study is @ruangguru. The results of the analysis showed that the public perception of Twitter users by using latent dirichlet allocation was formed into 28 topics.Keywords: latent dirichlet allocation, ruangguru, twitter.

Similarity Detection Using Latent Semantic Analysis Algorithm

2018 ◽  
Vol 6 (8) ◽  
pp. 102
Author(s):  
Priyanka R. Patil ◽  
Shital A. Patil
Keyword(s):  
Latent Semantic Analysis ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Mining Method ◽  
Research Papers ◽  
Information Measures ◽  
Automated Software ◽  
Day By Day ◽  
Ways Of Life ◽  
Dirichlet Allocation

Similarity View is an application for visually comparing and exploring multiple models of text and collection of document. Friendbook finds ways of life of clients from client driven sensor information, measures the closeness of ways of life amongst clients, and prescribes companions to clients if their ways of life have high likeness. Roused by demonstrate a clients day by day life as life records, from their ways of life are separated by utilizing the Latent Dirichlet Allocation Algorithm. Manual techniques can't be utilized for checking research papers, as the doled out commentator may have lacking learning in the exploration disciplines. For different subjective views, causing possible misinterpretations. An urgent need for an effective and feasible approach to check the submitted research papers with support of automated software. A method like text mining method come to solve the problem of automatically checking the research papers semantically. The proposed method to finding the proper similarity of text from the collection of documents by using Latent Dirichlet Allocation (LDA) algorithm and Latent Semantic Analysis (LSA) with synonym algorithm which is used to find synonyms of text index wise by using the English wordnet dictionary, another algorithm is LSA without synonym used to find the similarity of text based on index. LSA with synonym rate of accuracy is greater when the synonym are consider for matching.

scholarly journals Coherent structure identification in turbulent channel flow using latent Dirichlet allocation

2021 ◽  
Vol 920 ◽  
Author(s):  
Mohamed Frihat ◽  
Bérengère Podvin ◽  
Lionel Mathelin ◽  
Yann Fraigneau ◽  
François Yvon
Keyword(s):  
Channel Flow ◽  
Coherent Structure ◽  
Latent Dirichlet Allocation ◽  
Turbulent Channel Flow ◽  
Structure Identification ◽  
Dirichlet Allocation

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