Application of text mining employing k-means algorithms for clustering tweets of Tokopedia

Rejito, Juli; Atthariq, A.; Abdullah, Atje Setiawan

doi:10.1088/1742-6596/1722/1/012019

Cited by 5 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, k-means has achieved results on how to select the initial center K value and reduce the number of iterations [25][26][27][28][29][30]. However, due to the blindness of the initial center K value selection, the cluster number needs to be determined in advance, and there are problems such as local optimization.…”

Section: Related Technologymentioning

confidence: 99%

k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

Yan

et al. 2021

Complexity

View full text Add to dashboard Cite

At present, the explosive growth of data and the mass storage state have brought many problems such as computational complexity and insufficient computational power to clustering research. The distributed computing platform through load balancing dynamically configures a large number of virtual computing resources, effectively breaking through the bottleneck of time and energy consumption, and embodies its unique advantages in massive data mining. This paper studies the parallel k-means extensively. This article first initializes random sampling and second parallelizes the distance calculation process that provides independence between the data objects to perform cluster analysis in parallel. After the parallel processing of the MapReduce, we use many nodes to calculate distance, which speeds up the efficiency of the algorithm. Finally, the clustering of data objects is parallelized. Results show that our method can provide services efficiently and stably and have good convergence.

show abstract

Section: Related Technologymentioning

confidence: 99%

k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

Yan

et al. 2021

Complexity

View full text Add to dashboard Cite

show abstract

“…Rejito, Atthariq, and Abdullah's study explores the application of K-Means clustering in analyzing the tweet content of Tokopedia, a leading Indonesian e-commerce platform. Through text mining of 885 tweets, they identified 48 distinct clusters, which were further categorized into 5 major groups, revealing that tweets related to quizzes and prizes garnered the most engagement, while lifestyle content attracted the least [5]. Denny and Spirling's paper rigorously investigates the substantial influence of preprocessing decisions on the outcomes of unsupervised learning models in text analysis, specifically within political science research [6].…”

Section: Introduction (Literary Review)mentioning

confidence: 99%

Exploration of the Thematic Clustering and Collaboration Opportunities in Kazakhstani Research

Biloshchytskyi,

Shamgunova,

Biloshchytska

2024

sjaitu

View full text Add to dashboard Cite

In today's academic environment, the rapid growth of research publications calls for advanced methods to organize and understand the extensive collections of academic work. This study aims to systematically categorize a substantial number of research paper abstracts from Kazakhstani institutions, focusing on identifying key themes and potential interdisciplinary collaboration opportunities. The dataset includes 13,356 abstracts from the Scopus database, covering a wide range of academic fields. The methodology of this research goes beyond traditional hand-done analysis by using advanced text analysis tools to organize the text data efficiently. This initial phase is crucial for summarizing each abstract's core content. The next steps of the analysis use this organized data to find and group similar thematic areas, considering the complex and multi-dimensional nature of academic research topics. The results reveal a diverse array of research themes, highlighting the dynamic academic contributions from Kazakhstan. Significant areas such as environmental science, technological advancements, linguistics, and cultural studies are among the prominent clusters identified. These insights not only provide an overview of current research directions but also highlight the potential for cross-disciplinary partnerships. Moreover, the findings have important implications for decision-makers, scholars, and educational institutions by illuminating key research areas and collaborative possibilities. This thematic overview acts as a guide for shaping research policies, fostering academic connections, and efficiently distributing resources within the scholarly community. Ultimately, this study adds to the academic conversation by offering a way to navigate and utilize the wealth of information in scientific literature, promoting a more collaborative and integrated research environment.

show abstract

“…The DTM becomes the input for the K-Means algorithm which will group text by calculating the distance from the tweet vector to the centroid (center point) of the entire clusters and assigning the tweet vector into the closest cluster. Several studies that focus on analyzing Twitter data have carried out Text Clustering using the K-Means algorithm to obtain clusters or groups contained in a collection of tweets on various topics [7], [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

Text Clustering Online Learning Opinion During Covid-19 Pandemic in Indonesia Using Tweets

Tyas

Kurnia

Soleh

2022

BAREKENG: J. Math. & App.

View full text Add to dashboard Cite

To prevent the spread of corona virus, restriction of social activities are implemented including school activities which reaps the pros and cons in community. Opinions about online learning are widely conveyed mainly on Twitter. Tweets obtained can be used to extract information using text clustering to group topics about online learning during pandemic in Indonesia. K-Means is often used and has good performance in text clustering area. However, the problem of high dimensionality in textual data can result in difficult computations so that a sampling method is proposed. This paper aims to examine whether a sampling method to cluster tweets can result to an efficient clustering than using the whole dataset. After pre-processing, five sample sizes are selected from 28300 tweets which are 250, 500, 2500, 10000 and 20000 to conduct K-Means clustering. Results showed that from 10 iterations, three main cluster topics appeared 90%-100% in sample size of 2500, 10000 and 20000. Meanwhile sample size of 250 and 500 tend to produced 20%-60% appearance of the three main cluster topics. This means that around 8% to 35% of tweets used can yield representative clusters and efficient computation which is four times faster than using entire dataset.

show abstract

Application of text mining employing k-means algorithms for clustering tweets of Tokopedia

Cited by 5 publications

References 2 publications

k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

Exploration of the Thematic Clustering and Collaboration Opportunities in Kazakhstani Research

Text Clustering Online Learning Opinion During Covid-19 Pandemic in Indonesia Using Tweets

Contact Info

Product

Resources

About