Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform

Habibi, Muhammad; Cahyo, Puji Winar

doi:10.22146/ijccs.50574

Cited by 18 publications

(21 citation statements)

References 9 publications

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“…TF-IDF is a numerical statistical value that represents the relevance of word categorization in a particular document [15]. The following formula from TF-IDF as shown in Equation (1) [2]. 1Which is ",$ = is the weight of a term t in document d N = is the total number of documents " = is the number of documents containing term t. The weighting using TF-IDF is continued to search for suitable topics based on hashtag usage.…”

Section: Topic Modeling Processmentioning

confidence: 99%

“…Grouping data using cluster methods is done on Instagram hashtag caption analysis. The results of the report can find out the best hashtag suggestions to apply for the promotion of a product or service [2]. Other studies, using classification methods to determine the condition of disease severity in an area by using tweet text, the results of the survey can indicate the location with severity level so that it can be used as one of the parameters of rapid decision-making [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Popular Hashtags on Instagram Account The Ministry of Health

Cahyo¹,

Habibi²,

Priadana³

et al. 2021

Proceedings of the International Conference on Health and Medical Sciences (AHMS 2020)

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Using the Instagram platform to distribute information on this decade is considered quite useful. The Ministry of Health Republic Indonesia has an Instagram account with the username kemenkes_ri used as a medium for distributing health information. That information delivers through Instagram posts always includes hashtags that are used as search keywords for the content that has been distributed. Therefore, analysis is performed on all posts made by kemenkes_ri for one year in 2019. From hashtag monitoring can be seen the total frequency of posts every month for one year and top 10 hashtags that are often used. Furthermore, From the results of the most popular hashtag can proceed to search for topics that are often discussed using a combination of bigram features, weighting Term Frequency-Inverse Document Frequency (TF-IDF), followed by Topic Modeling using Latent Dirichlet Allocation (LDA).

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Section: Topic Modeling Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Popular Hashtags on Instagram Account The Ministry of Health

Cahyo¹,

Habibi²,

Priadana³

et al. 2021

Proceedings of the International Conference on Health and Medical Sciences (AHMS 2020)

Self Cite

View full text Add to dashboard Cite

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“…Extracting information on the Instagram platform is often necessary to determine how actor activities impact his followers [23]. In the Twitter platform, extracting information is generally carried out on actors' involvement on a conversation topic, which is generally marked with the hashtag # [24]. From the conversation topic, actors can be obtained by detecting named entities [25] or detecting mention @ in tweet content.…”

Section: Entity Profilingmentioning

confidence: 99%

Entity Profiling to Identify Actor Involvement in Topics of Social Media Content

Cahyo¹,

Habibi²

2020

Indonesian J. Comput. Cybern. Syst.

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The efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will impact the amount of data on social media will be. The data can be analyzed to get the influence of actors (account mentions) on the conversation. The power of an actor can be measured from how often the actor is mentioned in the conversation. This paper aims to conduct entity profiling on social media content to analyze an actor's influence on discussion. Furthermore, using sentiment analysis can determine the sentiment about an actor from a conversation topic. The Latent Dirichlet Allocation (LDA) method is used for analyzes topic modeling, while the Support Vector Machine (SVM) is used for sentiment analysis. This research can show that topics with positive sentiment are more likely to be involved in disaster management accounts, while topics with negative sentiment are more towards involvement in politicians, critics, and online news.

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“…Penelitian ini menggunakan metode K-Means sebagai solusi untuk pengklasifikasian dari sebuah objek. Metode K-Means banyak digunakan dalam penelitian terkait dengan pengklasteran data, diantaranya adalah penelitian terkait klasterisasi user karakteristik berdasarkan hashtag pada platform Instagram [6], klasterisasi tipe pembelajar sebagai parameter evaluasi kualitas pendidikan di perguruan tinggi [7], pengelompokan kunjungan wisatawan ke objek wisata unggulan di Prov. DKI Jakarta [8], serta digunakan untuk kuantisasi foto peradangan pada kulit [9].…”

Section: Latar Belakangunclassified

Analysis of Marketplace Conversation Trends on Twitter Platform Using K-Means

Nasron

Habibi

2020

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Businesses began to switch from the process of marketing products using conventional media to the process of marketing products using internet and social media. The switch of businesspeople to the process of marketing products through the internet and social media is because the marketing costs are cheaper than using conventional media. The problem that is often faced by business people when marketing products on social media is that they rarely see a marketplace that is becoming a trend and is being discussed by consumers on social media, so the marketing process is carried out less than the maximum. This study aims to analyze conversation trends related to the marketplace on the social media platform Twitter. The method used in this study is the K-Means Clustering method. This research succeeded in analyzing conversation trends related to the marketplace so that it can be used as a basis for consideration of businesspeople in choosing a marketplace. Marketplace trend analysis results show that Shopee, Lazada, and Tokopedia are marketplaces that are the subject of conversation on the social media platform Twitter.

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Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform

Cited by 18 publications

References 9 publications

Analysis of Popular Hashtags on Instagram Account The Ministry of Health

Analysis of Popular Hashtags on Instagram Account The Ministry of Health

Entity Profiling to Identify Actor Involvement in Topics of Social Media Content

Analysis of Marketplace Conversation Trends on Twitter Platform Using K-Means

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