scholarly journals PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

2022 ◽  
Vol 10 (4) ◽  
pp. 583-593
Author(s):  
Syiva Multi Fani ◽  
Rukun Santoso ◽  
Suparti Suparti
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Virtual Networks ◽  
Number Of Clusters ◽  
Silhouette Coefficient ◽  
Twitter Account ◽  
Computer Based ◽  
Twitter Users ◽  
Clustering Data ◽  
Coefficient Method

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

scholarly journals Analysis of User Sentiment of Twitter to Draft KUHP

2020 ◽  
Vol 16 (3) ◽  
pp. 273
Author(s):  
Nawang Indah Cahyaningrum ◽  
Danty Welmin Yoshida Fatima ◽  
Wisnu Adi Kusuma ◽  
Sekar Ayu Ramadhani ◽  
Muhammad Rizqi Destanto ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
The Other ◽  
Mining Methods ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Bahasa Indonesia

Twitter is one of social media where its user can share many responses for a phenomenon through a tweet. This research used 5000 tweets from Twitter users in Bahasa Indonesia with keyword “RUU KUHP(Draft Law of KUHP)” from 16th of September until 22nd of September 2019. That tweets were processed using Rstudio software with sentiment analysis that is one of Text Mining methods. This research aims to classify Twitter users’ responses to RUU KUHP to be negative sentiment, poisitive negative, and neutral. Also, this research also aims to know about topics’ frequencies that were related to RUU KUHP through visualization with bar plot and also wordcloud. This research also aims to know words that are associated with the most frequent words. Form this research, can be known that Twitter users’ responses to RUU KUHP tend to have neutral sentiment that means they did not take side between agreeing or disagreeing. From this research, also can be known about 10 most frequent words, there are kpk, tunda, dpr, pasal, kesal, jokowi, presiden, masuk, ya, and sahkan. Beside that, can be known the other words that are associated with them and also their probability.

scholarly journals Connecting the Urological Community : The #UroSoMe Experience

2019 ◽  
Vol 2 (2) ◽  
pp. e20-e29 ◽  
Author(s):  
Kalyan Gudaru ◽  
Leonardo Tortolero Blanco ◽  
Daniele Castellani ◽  
Hegel Trujillo Santamaria ◽  
Marcela Pelayo-Nieto ◽  
...  
Keyword(s):  
Social Media ◽  
Working Group ◽  
Efficient Manner ◽  
The Social ◽  
Social Media Platforms ◽  
Twitter Account ◽  
Group Members ◽  
Twitter Users ◽  
Use Of Social Media ◽  
Twitter Activity

Background and Objectives There is an increasing use of social media amongst the urological community. However, it is difficult to identify urological data on various social media platforms in an efficient manner. We proposed a hashtag, #UroSoMe, to be used when posting urology-related content in the social media platforms. The objectives of this article are to describe how #UroSoMe was developed, and to report the data of the first month of #UroSoMe.   Material and Methods The hashtag, #UroSoMe, was introduced to the urological community. The #UroSoMe working group was formed, and the members actively invited and encouraged people to use the hashtag #UroSoMe when posting urology-related contents. After the #UroSoMe (@so_uro) platform on twitter had grown to more than 300 users, the first live event of online case discussion, i.e. #LiveCaseDiscussions, was conducted. A prospective observational study of the hashtag #UroSoMe Twitter activity during the first month of its usage from 14 December 2018 to 13 January 2019 was evaluated. Outcome measures included number of users, number of tweets, user location, top tweeters, top hashtags used and interactions. Analysis was performed using NodeXL (Social Media Research Foundation; California, USA; https://www.smrfoundation.org/nodexl/), Symplur (https:// www.symplur.com) and Twitonomy (https://www.twitonomy.com).   Results The first month of #UroSoMe activity documented 1373 tweets/retweets by 1008 tweeters with 17698 mentions and 1003 replies. The #LiveCaseDiscussions was able to achieve a potential reach of 2,033,352 Twitter users. The top tweets mainly included cases presented by #UroSoMe working group members during #LiveCaseDiscussions. The twitonomy map showed participation from 214 geographical locations. The major groups of participants using the hashtag #UroSoMe were ‘Researcher/Academic’ and ‘Doctor’. The twitter account of #UroSoMe (@so_uro) has now grown to more than 1000 followers.   Conclusions Social media is an excellent platform for interaction amongst the urological community. The results demonstrated that #UroSoMe was able to achieve wide spread engagement from all over the world.

scholarly journals Gaya Komunikasi Politik Twitter Partai Gerindra

2021 ◽  
Vol 5 (2) ◽  
pp. 105-113
Author(s):  
Ayu Nenden Assyfa Putri
Keyword(s):  
Social Media ◽  
Political Communication ◽  
Information Diffusion ◽  
Diffusion Theory ◽  
Political Campaigns ◽  
Communication Style ◽  
Political Organizations ◽  
Great Opportunity ◽  
Twitter Account ◽  
Twitter Users

Social media, especially Twitter, as one of the most widely used platforms on the internet, is now being used by political organizations to convey their political communication messages. This study uses a descriptive qualitative method by analyzing the communication style conveyed by the Gerindra party Twitter account to its followers. In looking for references, researchers use a systematic review method wherein the authors must describe the search to be used, determine where and when they should search, and what terms they should use. The results of this study indicate that the style of political communication conveyed by the Gerindra Twitter account has the aim of being accepted by Twitter users, whose users are young people. Unlike the political communication messages conveyed during the 2014 & 2019 elections, the Gerindra Twitter account conveys its political communication style in a relaxed and informative way. The relevance of the information diffusion theory in this study is when the Gerindra party takes advantage of the great opportunity of Twitter as a social media to communicate its political campaigns so that new voters can accept it in the future.

scholarly journals Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model

2019 ◽  
Vol 8 (2) ◽  
pp. 78-83
Author(s):  
Novianti Puspitasari ◽  
Joan Angelina Widians ◽  
Noval Bayu Setiawan
Keyword(s):  
Customer Loyalty ◽  
Customer Segmentation ◽  
Number Of Clusters ◽  
Transaction Data ◽  
Model Based ◽  
Silhouette Coefficient ◽  
Rfm Model ◽  
A Company ◽  
Coefficient Method

Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algorithm can form the best customer segmentation into three groups, namely Occasional, Typical, and Gold, with a silhouette coefficient of 0.58132.

Malcolm Turnbull’s conversational career on Twitter: the case of the Australian Prime Minister and the NBN

2018 ◽  
Vol 167 (1) ◽  
pp. 88-104 ◽  
Author(s):  
Glen Fuller ◽  
Angus Jolly ◽  
Caroline Fisher
Keyword(s):  
Social Media ◽  
Prime Minister ◽  
Sharp Decline ◽  
Long Duration ◽  
Broadband Network ◽  
Twitter Account ◽  
Political Journalists ◽  
Twitter Users ◽  
Use Of Social Media ◽  
The Impact

Politicians’ use of Twitter during election periods has been extensively researched. There has been less scholarly focus on the way politicians’ use of Twitter changes depending on their political circumstances. This article reports on an analysis of Malcolm Turnbull’s Twitter account from October 2008 to July 2016 examining his ‘engagement’ in terms of ‘conversations’ with political journalists, specialist technology writers and other Twitter users. It found Turnbull ‘conversed’ with the general public more than elites and revealed heated exchanges with specialist technology writers about the National Broadband Network (NBN) and more genial ‘banter’ with political journalists. It also showed a peak in ‘conversations’ when he was Shadow Minister for Communications and a sharp decline once he became Minister for Communications and then Prime Minister. This article points to the need for further long-duration research to better understand the impact of changing political contexts on politicians’ use of social media.

scholarly journals Are Black Friday Deals Worth It? Mining Twitter Users’ Sentiment and Behavior Response

2019 ◽  
Vol 5 (3) ◽  
pp. 58 ◽  
Author(s):  
Saura ◽  
Reyes-Menendez ◽  
Palos-Sanchez
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Latent Dirichlet Allocation ◽  
Allocation Model ◽  
Media Communication ◽  
Customer Support ◽  
Latent Dirichlet Allocation Model ◽  
Twitter Users ◽  
And Behavior ◽  
Black Friday

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area.

scholarly journals Implementation Of Text Mining For Emotion Detection Using The Lexicon Method (Case Study: Tweets About Covid-19)

Telematika ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 49
Author(s):  
Agus Sasmito Aribowo ◽  
Siti Khomsah
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Emotion Detection ◽  
Public Sentiment ◽  
Twitter Users

Information and news about Covid-19 received various responses from social media users, including Twitter users. Changes in netizen opinion from time to time are interesting to analyze, especially about the patterns of public sentiment and emotions contained in these opinions. Sentiment and emotional conditions can illustrate the public's response to the Covid-19 pandemic in Indonesia. This research has two objectives, first to reveal the types of public emotions that emerged during the Covid-19 pandemic in Indonesia. Second, reveal the topics or words that appear most frequently in each emotion class. There are seven types of emotions to be detected, namely anger, fear, disgust, sadness, surprise, joy, and trust. The dataset used is Indonesian-language tweets, which were downloaded from April to August 2020. The method used for the extraction of emotional features is the lexicon-based method using the EmoLex dictionary. The result obtained is a monthly graph of public emotional conditions related to the Covid-19 pandemic in the dataset.

scholarly journals Large-scale identification and characterization of scholars on Twitter

2020 ◽  
pp. 1-21 ◽  
Author(s):  
Rodrigo Costas ◽  
Philippe Mongeon ◽  
Márcia R. Ferreira ◽  
Jeroen van Honk ◽  
Thomas Franssen
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Web Of Science ◽  
Twitter Data ◽  
Twitter Account ◽  
The Common ◽  
Twitter Users ◽  
Opening Up ◽  
Identification And Characterization

This paper presents a new method for identifying scholars who have a Twitter account from bibliometric data from Web of Science (WoS) and Twitter data from Altmetric.com . The method reliably identifies matches between Twitter accounts and scholarly authors. It consists of a matching of elements such as author names, usernames, handles, and URLs, followed by a rule-based scoring system that weights the common occurrence of these elements related to the activities of Twitter users and scholars. The method proceeds by matching the Twitter accounts against a database of millions of disambiguated bibliographic profiles from WoS. This paper describes the implementation and validation of the matching method, and performs verification through precision-recall analysis. We also explore the geographical, disciplinary, and demographic variations in the distribution of scholars matched to a Twitter account. This approach represents a step forward in the development of more advanced forms of social media studies of science by opening up an important door for studying the interactions between science and social media in general, and for studying the activities of scholars on Twitter in particular.

Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media

2021 ◽  
Vol 12 (2) ◽  
pp. 1-15
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Text Mining ◽  
Mental Health Problems ◽  
Negative Impact ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Nearest Neighbours ◽  
Twitter Users

A recent British study of people between the ages of 14 and 35 has shown that social media has a negative impact on mental health. The purpose of the paper is to detect people with mental disorders' behavior in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia, etc. For this, the author used text mining and machine learning algorithms (naïve Bayes, k-nearest neighbours) to analyse tweets. The obtained results were validated using different evaluation measures such as f-measure, recall, precision, entropy, etc.

scholarly journals IMPLEMENTASI ALGORITMA MODIFIED GUSTAFSON-KESSEL UNTUK CLUSTERING TWEETS PADA AKUN TWITTER LAZADA INDONESIA

2019 ◽  
Vol 8 (3) ◽  
pp. 285-295
Author(s):  
Ratna Kencana Putri ◽  
Budi Warsito ◽  
Mustafid Mustafid
Keyword(s):  
Social Media ◽  
Partition Coefficient ◽  
Clustering Algorithm ◽  
Optimum Number ◽  
Number Of Clusters ◽  
Online Social Media ◽  
Internet Users ◽  
Algorithm Validation ◽  
Twitter Account ◽  
Product Segmentation

Online social media is a new kind of media which is steadily growing and has become publicly popular. Due to its ability to spread informations rapidly and its easiness to access for internet users, social media provides new alternative to conduct advertising and product segmentation. Twitter is one of the most favored social media with 19.5 million users in Indonesia to the date. In this research, the application of text mining to cluster tweets from the @LazadaID Twitter account is done using the Modified Gustafson-Kessel clustering algorithm. The clustering process is executed five times with the number of cluster starts from two to six cluster. The results of this research indicate that the optimum number of clusters formed based on the Partition Coefficient and Classification Entropy validation index are three clusters. Those three clusters are tweets containing electronic stuff offers, discounts, and prize quizes. Tweets with the most retweets and likes are prize quiz tweets. PT Lazada Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @LazadaID Twitter account followers.Keywords: Twitter, advertising, Lazada Indonesia, Gustafson-Kessel Clustering algorithm, validation index

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