Technology Disruption in the Time of the Digital Ecosystem Society's Adoption

Setiana, Desi; Norsarah, Siti; Besar, Norainna; Anna, Tiro; Nasution, Marlina; Susanto, Heru

doi:10.4018/978-1-7998-8524-5.ch013

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Participants shared that after being cyberbullied, they felt humiliated. Similarly, participants from other studies about cyberbullying also shared a similar sentiment [17]. The feeling of humiliation stems from a feeling of degradation of the victim as cyberbullying damages the pride and dignity of a person [18].…”

Section: Victim Experiences Of Cyberbullyingmentioning

confidence: 69%

Cybercitizen’s Experience on Cyber Attacks and Threats from Online Platforms Amidst Pandemic and Community Lockdown

Muico¹,

Villasin²,

Casia³

et al. 2023

JLS

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This study sought to uncover the experience cybercitizen’s who experienced cyberattacks amidst pandemic and community lockdown. More specifically, it focused on the experience of the youth, coping strategies, and insights on cyberbullying. The study is qualitative by nature with 7 identified participants. The results show various means of cyberbullying experienced by the participants. Moreover, the participants also shared different coping strategies to deal with their experience. Furthermore, participants shared insights on their experiences.

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Section: Victim Experiences Of Cyberbullyingmentioning

confidence: 69%

Cybercitizen’s Experience on Cyber Attacks and Threats from Online Platforms Amidst Pandemic and Community Lockdown

Muico¹,

Villasin²,

Casia³

et al. 2023

JLS

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“…Additionally, selecting appropriate sentiment signals for real-time analysis is crucial in this process [26,27]. The increasing prevalence of textual content sharing on social media has led to an increase in the use of text-mining and sentiment analysis techniques [28][29][30].…”

Section: Background Of Studymentioning

confidence: 99%

Evaluating User Satisfaction Using Deep-Learning-Based Sentiment Analysis for Social Media Data in Saudi Arabia’s Telecommunication Sector

Alshamari

2023

Computers

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Social media has become common as a means to convey opinions and express the extent of satisfaction and dissatisfaction with a service or product. In the Kingdom of Saudi Arabia specifically, most social media users share positive and negative opinions about a service or product, especially regarding communication services, which is one of the most important services for citizens who use it to communicate with the world. This research aimed to analyse and measure user satisfaction with the services provided by the Saudi Telecom Company (STC), Mobily, and Zain. This type of sentiment analysis is an important measure and is used to make important business decisions to succeed in increasing customer loyalty and satisfaction. In this study, the authors developed advanced methods based on deep learning (DL) to analyse and reveal the percentage of customer satisfaction using the publicly available dataset AraCust. Several DL models have been utilised in this study, including long short-term memory (LSTM), gated recurrent unit (GRU), and BiLSTM, on the AraCust dataset. The LSTM model achieved the highest performance in text classification, demonstrating a 98.04% training accuracy and a 97.03% test score. The study addressed the biggest challenge that telecommunications companies face: that the company’s services influence customers’ decisions due to their dissatisfaction with the provided services.

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“…Text mining is a difficult work and focuses on topical words of different topics and sentiment analysis is necessary to classify it into positive and negative polarity, and to select sentiment signals for real-time analysis [7,8]. As consumers continue to share textual material on social media, text mining and sentiment analysis are becoming more popular as mentioned by [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Innovative Business Process Reengineering Adoption: Framework of Big Data Sentiment, Improving Customers’ Service Level Agreement

Susanto

Sari

Leu

2022

BDCC

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Social media is now regarded as the most valuable source of data for trend analysis and innovative business process reengineering preferences. Data made accessible through social media can be utilized for a variety of purposes, such as by an entrepreneur who wants to learn more about the market they intend to enter and uncover their consumers’ requirements before launching their new products or services. Sentiment analysis and text mining of telecommunication businesses via social media posts and comments are the subject of this study. A proposed framework will be utilized as a guideline, and it will be tested for sentiment analysis. Lexicon-based sentiment categorization is used as a model training dataset for a supervised machine learning support vector machine. The result is very promising. The accuracy and the quantity of the true sentiments it can detect are compared. This result signifies the usefulness of text mining and sentiment analysis on social media data, while the use of machine learning classifiers for predicting sentiment orientation provides a useful tool for operations and marketing departments. The availability of large amounts of data in this digitally active society is advantageous for sectors such as the telecommunication industry. These companies can be two steps ahead with their strategy and develop a more cohesive company that can make customers happier and mitigate problems easily with the use of text mining and sentiment analysis for further adopting innovative business process reengineering for service improvements within the telecommunications industry.

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Technology Disruption in the Time of the Digital Ecosystem Society's Adoption

Cited by 8 publications

References 20 publications

Cybercitizen’s Experience on Cyber Attacks and Threats from Online Platforms Amidst Pandemic and Community Lockdown

Cybercitizen’s Experience on Cyber Attacks and Threats from Online Platforms Amidst Pandemic and Community Lockdown

Evaluating User Satisfaction Using Deep-Learning-Based Sentiment Analysis for Social Media Data in Saudi Arabia’s Telecommunication Sector

Innovative Business Process Reengineering Adoption: Framework of Big Data Sentiment, Improving Customers’ Service Level Agreement

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