Sentiment Analysis on PeduliLindungi Application Using TextBlob and VADER Library

Illia, Fathonah; Eugenia, Migunani Puspita; Rutba, Sita Aliya

doi:10.34123/icdsos.v2021i1.236

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…Research by Illia et al [14] was conducted to get public sentiment toward the application using Twitter data. The data collection period was from 31 August to 7 September 2021, and this period was chosen because of the emergence of news regarding vaccine data leaks related to data leaks in the PeduliLindung application.…”

Section: Related Researchsmentioning

confidence: 99%

“…This research is distinct in that it evaluates community responses to a specific government activity (Regsosek) and uses a combination of sentiment analysis and topic modeling techniques. The study builds on the existing research by using similar sentiment analysis techniques (as seen in research [13] and [14]) and topic modeling methods (as seen in research [12]) to understand how the community perceives and responds to a government initiative. The use of various classification techniques and the comparison of their performance further contribute to the field of sentiment analysis.…”

Section: Related Researchsmentioning

confidence: 99%

See 1 more Smart Citation

A Sentiment Analysis and Topic Modelling of The Socio-Economic Registration 2022

Simbolon,

Manurung,

Andini

et al. 2023

icdsos

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Socio-Economic Registration or Regsosek is an activity of Statistics Indonesia (BPS) that aims to collect data related to the profile, social and economic conditions, and welfare levels of all residents in 514 regencies/cities in Indonesia. One indicator of the success of Regsosek 2022 is the response and opinion from the community regarding the activity. The response and opinion can provide an overview of the implementation of Regsosek 2022 so that the picture can be used as a lesson learned to carry out the following population data collection. This study uses several methods to analyze the results of community responses and opinions on Regsosek activities, especially on Twitter social media. The method used in this research is sentiment analysis classification with four techniques: Naïve Bayes, Nearest Centroid, K-Nearest Neighbors, and Support Vector Machine. Then, the performance of the four techniques will be compared. In addition, the topic modeling method will also be used with two techniques, namely Latent Semantic Analysis and Latent Dirichlet Allocation. Data is collected using web scraping techniques. The results obtained from the sentiment analysis classification are that the Nearest Centroid method provides the best results with a relatively high and balanced f1-score value in positive and negative sentiments, which are 59% and 66%, respectively. Moreover, LDA modeling results are better than the LSA method for topic modeling results.

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Section: Related Researchsmentioning

confidence: 99%

Section: Related Researchsmentioning

confidence: 99%

A Sentiment Analysis and Topic Modelling of The Socio-Economic Registration 2022

Simbolon,

Manurung,

Andini

et al. 2023

icdsos

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“…Text sentiment polarity and intensity To evaluate intensity or the sentiment score of the text and text polarity the VADER sentiment analyzer (Hutto and Gilbert, 2014), available in Python's natural language toolkit is employed. VADER is observed to work better in the language used by the users on social media (Illia et al, 2021), (Bonta and Janardhan, 2019). Users tend to communicate informally, using slang, abbreviations, capitalization and punctuation marks, etc.…”

Section: System Overviewmentioning

confidence: 99%

DUCS at SemEval-2022 Task 6: Exploring Emojis and Sentiments for Sarcasm Detection

Grover¹,

Banati²

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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This paper describes the participation of team DUCS at SemEval 2022 Task 6: iSarcasmEval -Intended Sarcasm Detection in English and Arabic. Team DUCS participated in SubTask A of iSarcasmEval which was to determine if the given English text was sarcastic or not. In this work, emojis were utilized to capture how they contributed to the sarcastic nature of a text. It is observed that emojis can augment or reverse the polarity of a given statement. Thus sentiment polarities and intensities of emojis, as well as those of text, were computed to determine sarcasm. Use of capitalization, word repetition, and use of punctuation marks like '!' were factored in as sentiment intensifiers. An NLP augmenter was used to tackle the imbalanced nature of the sarcasm dataset. Several architectures comprising of various ML and DL classifiers, and transformer models like BERT and Multimodal BERT were experimented with. It was observed that Multimodal BERT outperformed other architectures tested and achieved an F1-score of 30.71%. The key takeaway of this study was that sarcastic texts are usually positive sentences. In general emojis with positive polarity are used more than those with negative polarities in sarcastic texts.

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“…These applications store confidential data that should be guaranteed security. However, the PeduliLindungi application, which holds the National Identity Number (NIK) and vaccine registration number on the COVID-19 vaccination certificate [6] is suspected that there was a data leak due to the circulation of data by the President of the Republic of Indonesia, Joko Widodo [7]. Apps like 1) Tokopedia; 2) Bukalapak; 3) Bhinneka; and 4) JKN Mobile also experienced data leaks [7].…”

Section: Introductionmentioning

confidence: 99%

A Prototype of Decentralized Applications (DApps) Population Management System Based on Blockchain and Smart Contract

Saian,

Sembiring,

Manongga

2024

JOIV : Int. J. Inform. Visualization

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The Indonesian population reached 270,20 million in 2020. Each resident is equipped with various secret identities. The COVID-19 pandemic has made all activities use technology as a basis, causing residents' identities to be stored digitally. Some applications that keep these identities experience data leaks. However, with the advent of Web3 and its emphasis on decentralization through blockchain, a new era of secure data management is possible. Blockchain, with its inherent security features, ensures that data stored is secure, difficult to damage or lose due to mutual consensus. Every transaction is recorded, making it easy to carry out the audit process. Therefore, this research will design and implement prototype dApps for secure population management, leveraging the superior security of blockchain technology. The initial stage of research is to conduct a literature study. Furthermore, it is to create designs such as system, infrastructure, and activity diagrams. Then do the development of the dApps prototype. The last is testing using OWASP ZAP and cost analysis. A dApps prototype was implemented on a blockchain. Every transaction is recorded and publicly viewable through the Etherscan platform. Other data stored on a blockchain have gone through an AES-256 encryption process with the data owner's account key so that the owner can only see the data. The results of the tests performed show that there is no high-level warning. The cost analysis results show that the most used costs are when deploying smart contracts and making new data. For further development, it is implementing permissionless blockchain and multi-accounts.

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Sentiment Analysis on PeduliLindungi Application Using TextBlob and VADER Library

Cited by 14 publications

References 4 publications

A Sentiment Analysis and Topic Modelling of The Socio-Economic Registration 2022

A Sentiment Analysis and Topic Modelling of The Socio-Economic Registration 2022

DUCS at SemEval-2022 Task 6: Exploring Emojis and Sentiments for Sarcasm Detection

A Prototype of Decentralized Applications (DApps) Population Management System Based on Blockchain and Smart Contract

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