Sentiment Analysis for Super Applications in Indonesia: A Case Study of Gov2Go App

Pilliang, Marzuki; Akbar, Habibullah; Firmansyah, Gerry

doi:10.1109/iconeei55709.2022.9972291

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Sentiment analysis processes unstructured words found in a word or sentence to predict the expressive value in positive, negative, or neutral responses [1]. Sentiment analysis seeks to determine the polarity of text by measuring its level of subjectivity, which can result in a positive, neutral, or negative value [2]. The sentiment analysis research on the content of PERMENKOMINFO No.5 of the Year 2020 aims to identify public sentiment towards the policies outlined in it.…”

Section: Sentiment Analysis Of Content Permenkominfo No5 Of 2020 Usin...mentioning

confidence: 99%

Sentiment Analysis of Content Permenkominfo NO.5 of 2020 Using a Classification Algorithm

Amada

Munawar

Pilliang

2023

jitk

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This study aims to evaluate the impact of the policy issued by the Minister of Communication and Information Technology (PERMENKOMINFO No.5 of 2020) on the public's ability to access content through Private Scope Electronic System Providers (PSE). The study uses sentiment analysis and data classification methods to analyze the content of PERMENKOMINFO No.5 of 2020 and provides results on the accuracy of sentiment prediction. The results of the study show that the data classification method in sentiment analysis can provide accurate results in predicting the sentiment towards the content of PERMENKOMINFO No.5 of 2020. The study also highlights the need for improvement and better policy to ensure the interests of the public in accessing online information. The negative sentiment of 80.34% obtained through sentiment analysis provides important contributions for policy evaluation and feedback for improvement. This study provides valuable insights into the public's sentiment towards the PERMENKOMINFO No.5 of 2020 policy and its impact on their ability to access content. It also contributes to understanding the legal uncertainty in accessing content and reinforces the case for better policy to ensure the interests of the public.

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Section: Sentiment Analysis Of Content Permenkominfo No5 Of 2020 Usin...mentioning

confidence: 99%

Sentiment Analysis of Content Permenkominfo NO.5 of 2020 Using a Classification Algorithm

Amada

Munawar

Pilliang

2023

jitk

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Enhancing Sentiment Analysis with GPT—A Comparison of Large Language Models and Traditional Machine Learning Techniques

Obinwanne,

Brandtner

2024

Lecture Notes in Networks and Systems

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From Customer’s Voice to Decision-Maker Insights: Textual Analysis Framework for Arabic Reviews of Saudi Arabia’s Super App

Alrayani,

Kalkatawi,

Abulkhair

et al. 2024

Applied Sciences

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Recently, business sectors have focused on offering a wide variety of services through utilizing different modern technologies such as super apps in order to fulfill customers’ needs and create a satisfactory user experience. Accordingly, studying the user experience has become one of the most popular trends in the research field due to its essential role in business prosperity and continuity. Thus, many researchers have dedicated their efforts to exploring and analyzing the user experience across social media, blogs, and websites, employing a variety of research methods such as machine learning to mine users’ reviews. However, there are limited studies concentrated on analyzing super app users’ experiences and specifically mining Arabic users’ reviews. Therefore, this paper aims to analyze and discover the most important topics that affect the user experience in the super app environment by mining Arabic business sector users’ reviews in Saudi Arabia using biterm topic modeling, CAMeL sentiment analyzer, and doc2vec with k-means clustering. We explore users’ feelings regarding the extracted topics in order to identify the weak aspects to improve and the strong aspects to enhance, which will promote a satisfactory user experience. Hence, this paper proposes an Arabic text annotation framework to help the business sector in Saudi Arabia to determine the important topics with negative and positive impacts on users’ experience. The proposed framework uses two approaches: topic modeling with sentiment analysis and topic modeling with clustering. As a result, the proposed framework reveals four important topics: delivery and payment, customer service and updates, prices, and application. The retrieved topics are thoroughly studied, and the findings show that, in most topics, negative comments outweigh positive comments. These results are provided with general analysis and recommendations to help the business sector to improve its level of services.

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Sentiment Analysis for Super Applications in Indonesia: A Case Study of Gov2Go App

Cited by 3 publications

References 17 publications

Sentiment Analysis of Content Permenkominfo NO.5 of 2020 Using a Classification Algorithm

Sentiment Analysis of Content Permenkominfo NO.5 of 2020 Using a Classification Algorithm

Enhancing Sentiment Analysis with GPT—A Comparison of Large Language Models and Traditional Machine Learning Techniques

From Customer’s Voice to Decision-Maker Insights: Textual Analysis Framework for Arabic Reviews of Saudi Arabia’s Super App

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