An analysis on the insights of the anti-vaccine movement from social media posts using k-means clustering algorithm and VADER sentiment analyzer

Garay, Juan A. Raygoza; Yap, Reuel Joab C; Sabellano, Mary Jane G.

doi:10.1088/1757-899x/482/1/012043

Cited by 16 publications

(7 citation statements)

References 10 publications

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“…Metode lexicon based adalah metode yang tidak memerlukan data training atau data yang telah dilabeli namun sudah tersedia dalam kamus lengkap dengan kepolaritasan sentimennya. Metode lexicon based menggunakan kamus, kosa kata, atau korpus yang sudah ada (yang telah dibuat oleh para ahli linguistik) dan mencocokkannya dengan data yang ingin diklasifikasikan [4]. Sedangkan metode machine learning menggunakan algoritma machine learning untuk mengklasifikasikan data yang ingin diklasifikasikan.…”

Section: Pendahuluanunclassified

“…VADER menggunakan kamus lexicon berbahasa inggris (en). VADER sentiment polarity detection digunakan untuk proses sentimen analisis karena proses penentuan sentimen yang mudah bersumber dari dictionary yang tersedia sehingga bisa mendekati penilaian manusia [4].…”

Section: Pendahuluanunclassified

“…Metode lexicon based menggunakan kamus dan kosakata yang sudah ada (yang telah dibuat oleh para ahli linguistik), lalu mencocokkannya dengan data yang ingin diklasifikasikan. Metode lexicon based tidak memerlukan data training atau data yang telah dilabeli namun sudah tersedia dalam kamus lengkap dengan kepolaritasan sentimennya [4]. Sedangkan metode machine learning menggunakan algoritma machine learning untuk mengklasifikasikan data yang ingin diklasifikasikan.…”

Section: Klasifikasiunclassified

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Analisis Sentimen Akun Twitter Apex Legends Menggunakan VADER

Abimanyu¹,

Budianita²,

Cynthia³

et al. 2022

JNKTI

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Abstrak - Pesatnya peningkatan jasa internet saat ini, ada banyak informasi yang dihasilkan dalam jumlah besar secara terus menerus dalam waktu yang singkat. Akhir-akhir ini, analisis sentimen dengan menggunakan ulasan dan pesan telah menjadi topik penelitian yang populer dibicarakan di bidang Natural Language Processing. Selama bertahun-tahun, permainan online telah menjadi suatu aktivitas yang tidak bisa dipisahkan dari sebagian besar orang. Apex Legends adalah salah satu contoh game yang sangat popular di seluruh dunia. Untuk mendapatkan informasi bagaimana pendapat para pemain tentang permainan ini diperlukan analisis sentimen. Pada penelitian ini dilakukan analisis sentimen menggunakan bantuan aplikasi Orange Data Mining dengan metode VADER pada akun twitter Apex Legends menggunakan data sebanyak 500 tweet. Pengujian data dilakukan dengan membandingkan hasil yang didapat menggunakan metode VADER dengan hasil pengujian pakar, yaitu native speaker dari Canada dan Amerika. VADER mengklasifikasikan data yang didapatkan melalui twitter berdasarkan nilai compound yang didapat. Penelitian ini menghasilkan kesimpulan yaitu perbandingan dari pengujian menggunakan VADER dan pengujian pakar tidak berbeda jauh, yang mana total persentase dari penggunaan metode VADER untuk menganalisis sentiment dari twitter ini adalah : Positif = 18%, Negatif = 4,6%, Netral = 73,6%. Sedangkan hasil pengujian pakar adalah : Positif = 27%, Negatif = 10,8%, Netral = 62,2%.Kata kunci: VADER, Apex Legends, Game, Twitter, Uji Pakar Abstract - With the rapid increase in internet services today, there is a lot of information produced in large quantities continuously in a short time. Recently, sentiment analysis using reviews and messages has become a popular research topic discussed in the Natural Language Processing field. Over the years, online gaming has become an activity that cannot be separated from most of the people. Apex Legends is one example of a game that is very popular around the world. To get information on how the players think about the game, sentiment analysis is needed. In this study, sentiment analysis was carried out using the Orange Data Mining application with the VADER method on the Apex Legends twitter account using 500 tweets (data). Data testing is done by comparing the results obtained using the VADER method with the results of expert testing, native speaker from Canada and America. VADER classifies the data obtained through twitter based on the compound value obtained. This study concludes that the comparison of testing using VADER and expert testing is not much different, where the total percentage of using the VADER method to analyze sentiment from Twitter is : Positive = 18%, Negative = 4,6%, Neutral = 73,6%. While the results of expert testing is : Positive = 27%, Negative = 10,8%, Neutral = 62,2%.Keywords : VADER, Apex Legends, Game, Twitter, Expert Test (Uji Pakar)

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Section: Pendahuluanunclassified

Section: Klasifikasiunclassified

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Analisis Sentimen Akun Twitter Apex Legends Menggunakan VADER

Abimanyu¹,

Budianita²,

Cynthia³

et al. 2022

JNKTI

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“…K-means clustering algorithm has been used to detect topics in social media data owing to its efficiency and easy implementation [13,56,57]. The K-means clustering is an unsupervised clustering algorithm that finds groups in data.…”

Section: Clustering Approachmentioning

confidence: 99%

Critical Image Identification via Incident-Type Definition Using Smartphone Data during an Emergency: A Case Study of the 2020 Heavy Rainfall Event in Korea

Choi

Kim²,

Hong³

et al. 2021

Sensors

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In unpredictable disaster scenarios, it is important to recognize the situation promptly and take appropriate response actions. This study proposes a cloud computing-based data collection, processing, and analysis process that employs a crowd-sensing application. Clustering algorithms are used to define the major damage types, and hotspot analysis is applied to effectively filter critical data from crowdsourced data. To verify the utility of the proposed process, it is applied to Icheon-si and Anseong-si, both in Gyeonggi-do, which were affected by heavy rainfall in 2020. The results show that the types of incident at the damaged site were effectively detected, and images reflecting the damage situation could be classified using the application of the geospatial analysis technique. For 5 August 2020, which was close to the date of the event, the images were classified with a precision of 100% at a threshold of 0.4. For 24–25 August 2020, the image classification precision exceeded 95% at a threshold of 0.5, except for the mudslide mudflow in the Yul area. The location distribution of the classified images showed a distribution similar to that of damaged regions in unmanned aerial vehicle images.

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“…Quantitatively, it is not known how many people are protesting against the COVID-19 vaccine. However, some researchers are starting to worry that their presence undermines efforts to massvaccinate [29]- [31]. This group is small but has an effective communication strategy and reaches the wider community.…”

Section: Introductionmentioning

confidence: 99%

What promotes cognitive dissonance among anti-vaccine members in Indonesia?

Thamrin

Bafadhal²,

Santoso³

2023

IJPHS

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This study aimed to examine the influence of expectations on vaccines, trust in government, perceived threats, and information overload to cognitive dissonance and its implications for vaccine use, behavioral negotiation, and information avoidance. After we formulated eight hypotheses, all of them was tested using the partial least squares structural equation modeling (PLS-SEM) method. This study involved 173 community members to fill out a questionnaire with 31 questions. The results showed that expectations of vaccines and information overload affected cognitive dissonance. In contrast, the perceived threat due to the COVID-19 pandemic and trust in the government did not affect cognitive dissonance. We also found that cognitive dissonance negatively and significantly affected vaccine use and positively and significantly affected behavior to continue negotiating and avoiding information related to COVID-19 vaccination. This study is among the first to examine members of the anti-vaccine community quantitatively and practically attempts to intervene in the anti-vaccine community so that they are cognitively dissonant are to increase expectations of vaccination and confuse them by presenting the information overload they receive.

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An analysis on the insights of the anti-vaccine movement from social media posts using k-means clustering algorithm and VADER sentiment analyzer

Cited by 16 publications

References 10 publications

Analisis Sentimen Akun Twitter Apex Legends Menggunakan VADER

Analisis Sentimen Akun Twitter Apex Legends Menggunakan VADER

Critical Image Identification via Incident-Type Definition Using Smartphone Data during an Emergency: A Case Study of the 2020 Heavy Rainfall Event in Korea

What promotes cognitive dissonance among anti-vaccine members in Indonesia?

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