Knowledge Discovery Based on Sentiment Analysis of Public Perceptions About Generative AI on X

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

The rapid growth of financial technology (fintech) services in Indonesia has significantly transformed digital transaction behavior, with digital wallets such as DANA becoming widely adopted. Despite high usage rates, public discourse on social media frequently highlights both positive experiences and recurring concerns related to system reliability and data security. Twitter, as a real-time public communication platform, provides a rich source of user-generated content that reflects public perception toward fintech services. This study investigates public sentiment toward DANA fintech services in Indonesia through sentiment analysis of Twitter data using machine learning approaches. Purpose:This study aims to analyze public perception of DANA fintech services based on sentiment expressed on Twitter and to compare the performance of several machine learning classification algorithms in identifying positive, neutral, and negative sentiments in Indonesian-language social media texts. Methods/Study design/approach: The study employs a quantitative descriptive approach using text mining and sentiment analysis techniques. Twitter data were collected via the Twitter API over a specified period and processed through preprocessing stages including text cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest algorithms. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Result/Findings:The results indicate that public sentiment toward DANA is predominantly positive, driven by perceptions of transaction convenience and speed, followed by neutral sentiment, while negative sentiment is mainly associated with system disruptions and data security issues. Among the evaluated algorithms, SVM achieved the highest classification accuracy (91.4%), outperforming Random Forest and KNN. These findings demonstrate that SVM is more effective for sentiment classification of short Indonesian-language texts on social media platforms. Novelty/Originality/Value:This study contributes to the growing body of sentiment analysis research in Indonesia by providing empirical evidence on public perception of fintech services using machine learning techniques. By systematically comparing multiple classification algorithms in the context of Indonesian Twitter data, the study offers methodological insights and practical implications for fintech service providers in improving service quality, communication strategies, and user trust.

BackgroundSocial media serves as a vast repository of data, offering insights into public perceptions and emotions surrounding significant societal issues. Amid the COVID-19 pandemic, long COVID (formally known as post–COVID-19 condition) has emerged as a chronic health condition, profoundly impacting numerous lives and livelihoods. Given the dynamic nature of long COVID and our evolving understanding of it, effectively capturing people’s sentiments and perceptions through social media becomes increasingly crucial. By harnessing the wealth of data available on social platforms, we can better track the evolving narrative surrounding long COVID and the collective efforts to address this pressing issue.ObjectiveThis study aimed to investigate people’s perceptions and sentiments around long COVID in Canada, the United States, and Europe, by analyzing English-language tweets from these regions using advanced topic modeling and sentiment analysis techniques. Understanding regional differences in public discourse can inform tailored public health strategies.MethodsWe analyzed long COVID–related tweets from 2021. Contextualized topic modeling was used to capture word meanings in context, providing coherent and semantically meaningful topics. Sentiment analysis was conducted in a zero-shot manner using Llama 2, a large language model, to classify tweets into positive, negative, or neutral sentiments. The results were interpreted in collaboration with public health experts, comparing the timelines of topics discussed across the 3 regions. This dual approach enabled a comprehensive understanding of the public discourse surrounding long COVID. We used metrics such as normalized pointwise mutual information for coherence and topic diversity for diversity to ensure robust topic modeling results.ResultsTopic modeling identified five main topics: (1) long COVID in people including children in the context of vaccination, (2) duration and suffering associated with long COVID, (3) persistent symptoms of long COVID, (4) the need for research on long COVID treatment, and (5) measuring long COVID symptoms. Significant concern was noted across all regions about the duration and suffering associated with long COVID, along with consistent discussions on persistent symptoms and calls for more research and better treatments. In particular, the topic of persistent symptoms was highly prevalent, reflecting ongoing challenges faced by individuals with long COVID. Sentiment analysis showed a mix of positive and negative sentiments, fluctuating with significant events and news related to long COVID.ConclusionsOur study combines natural language processing techniques, including contextualized topic modeling and sentiment analysis, along with domain expert input, to provide detailed insights into public health monitoring and intervention. These findings highlight the importance of tracking public discourse on long COVID to inform public health strategies, address misinformation, and provide support to affected individuals. The use of social media analysis in understanding public health issues is underscored, emphasizing the role of emerging technologies in enhancing public health responses.

PurposeThe purpose of the study was to evaluate the impact of generative artificial intelligence (GenAI) on students' learning experiences and perceptions through a master’s-level course. The study specifically focused on student engagement, comfort with GenAI and ethical considerations.Design/methodology/approachThe study used an action research methodology employing qualitative data collection methods, including pre- and post-course surveys, reflective assignments, class discussions and a questionnaire. The AI-Ideas, Connections, Extensions (ICE) Framework, combining the ICE Model and AI paradigms, is used to assess students' cognitive engagement with GenAI.FindingsThe study revealed that incorporating GenAI in a master’s-level instructional design course increased students' comfort with GenAI and their understanding of its ethical implications. The AI-ICE Framework demonstrated most students were at the initial engagement level, with growing awareness of GenAI’s limitations and ethical issues. Course reflections highlighted themes of improved teaching strategies, personal growth and the practical challenges of integrating GenAI responsibly.Research limitations/implicationsThe small sample size poses challenges to the analytical power of the findings, potentially limiting the breadth and applicability of conclusions. This constraint may affect the generalizability of the results, as the participants may not fully represent the broader population of interest. The researchers are mindful of these limitations and suggest caution in interpreting the findings, acknowledging that they may offer more exploratory insights than definitive conclusions. Future research endeavors should aim to recruit a larger cohort to validate and expand upon the initial observations, ensuring a more robust understanding.Originality/valueThe study is original in its integration of GenAI into a master's-level instructional design course, assessing both the practical and ethical implications of its use in education. By utilizing the AI-ICE Framework to evaluate students' cognitive engagement and employing action research methodology, the study provides insights into how GenAI influences learning experiences and perceptions. This approach bridges the gap between theoretical understanding and the real-world application of GenAI, offering actionable strategies for its responsible use in educational settings.

Media Sentiments on Stakeholders and Daily Abnormal Returns during COVID-19 Pandemic: Early Evidence from the US

Sentiment analysis is one part of natural language processing. Sentiment analysis can be done by lexicon based, or machine learning based. Sentiment analysis based on machine learning has advantage of dynamism to meet with new language datasets or new vocabulary. Sentiment analysis seeks to understand the sentiments contained in a sentence. A sentence can be positive, neutral or negative, based on its sentiments. A sentence can have positive, neutral or negative sentiments. However, the fact is each sentence does not always have positive, negative or neutral sentiment clearly. We try to develop a sentiment analysis method that can show the sentiment degree of a sentence. Fuzzy sentiment analysis using convolutional neural network are introduced in this paper to produce more accurate sentiment analysis results. Convolutional neural networks are a popular machine learning method for sentiment analysis. The concept of fuzzy sets is used to express the sentiment degree of a sentence. Euclidean distance analysis to determine the proximity of two vectors is used to show that this method is better than the standard method. The method we propose successfully produces a value that indicates the degree of sentiment of a sentence. Comparison of the euclid distance between the results of the standard sentiment analysis and our method shows that the results of the fuzzy sentiment analysis using convolutional neural network have a distance that is relatively close to the true sentiment value. Fuzzy convolutional neural network analysis sentiment is proven to be able to produce better and smoother sentiment analysis results than standard methods.

Background The application of generative artificial intelligence (GenAI) in education has been deepening. However, at the same time, behaviours that jeopardise academic health, such as learners' over‐reliance on generative AI and massive plagiarism of generated content of generative AI in essay writing, have begun to emerge, and the issue of generative AI ethics should not be underestimated. It is necessary to develop an in‐depth understanding of the issue of ethical adoption of generative AI for learners. Objectives This article examines the determinants of ethical adoption of generative artificial intelligence (GenAI) learning applications among college students. It explores the mechanisms through which these factors operate and investigates the moderating effects of key variables. Based on these findings, the study proposes targeted recommendations to foster responsible GenAI integration in education, offering valuable insights for the wider adoption of GenAI technologies in educational contexts. Methods This study constructs an ethical adoption model for college students' use of GenAI learning applications, integrating the technology acceptance model and the unified theory of acceptance and use of technology. Following the theoretical model development, empirical research was conducted—encompassing questionnaire surveys, quantitative data analysis and results interpretation—to validate the proposed framework. Results The results demonstrate that college students' intention to adopt ethical practices regarding generative AI, facilitating conditions and the management system exhibit a positive correlation with actual compliance with ethical norms. Among these factors, ethical intention exerts the strongest effect. Furthermore, students' performance expectation concerning the ethical adoption of generative AI is positively correlated with their ethical adoption intention. Gender, grade level, voluntariness of use and prior experience significantly moderate these influence pathways. Conclusions This study identifies key factors influencing college students' adoption of generative artificial intelligence (GenAI) in learning applications. The findings offer theoretical and practical insights to inform the responsible integration of GenAI technologies in educational settings.

With the advent of social media, particularly Twitter, public opinion has become a dynamic and influential force. This study explores the public's perception of Islamic higher education institutions on Twitter, employing sentiment analysis to decipher the nuances of public sentiment. A comprehensive quantitative research methodology employing sentiment analysis was applied to a dataset of 11,809 tweets collected from 2018 to 2022, all focused on perceptions of Islamic higher education. The preprocessing phase involved cleaning, case folding, punctuation, number removal, stopword elimination, and tokenization. Sentiment analysis techniques were utilized to categorize tweets into positive, negative, or neutral sentiments. The analysis revealed a prevalent positive sentiment, constituting 73.16% of the dataset, 21.37% exhibited negative sentiment, and 5.47% reflected neutral sentiment. Visualizations, including word clouds, highlighted key topics shaping public perception. Words like "university," "religion," and "student" emerged prominently, influencing sentiment across categories. These results underscore a positive public perception of Islamic higher education, indicating the effectiveness of social media in shaping favorable opinions. The research emphasizes the significance of social media, specifically Twitter, as a platform for fostering positive perceptions of Islamic higher education. Strategic communication, proactive content dissemination, and responsive engagement are essential for enhancing institutional image. Future research directions, including advanced methodologies like topic modeling and word association techniques, promise a deeper understanding of public sentiment nuances, guiding educational institutions and policymakers toward effective social media engagement strategies

Purpose The proliferation of large language models (LLMs) and generative artificial intelligence (GenAI) applications has provided ample opportunities for crime, including technology-facilitated financial crimes. The present study conducted a quantitative systematic literature review to examine the evolving intersection of GenAI and financial crime. Specifically, the study identified keyword concentrations, latent research topics, and thematic relationships within this emerging domain to explore how current scholarship understands the role of GenAI in financial crimes. Methods Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, this systematic literature review employed three quantitative analytical methods—bibliometric analysis, topic modelling, and knowledge graph analysis—to reveal trends, concentrations, and connections of prominent keywords and topics that emerged from the extant literature. Results With the assistance of the PRISMA 2020 framework, a total of 94 studies were incorporated for the quantitative systematic review. The bibliometric analysis identified five keyword clusters, while the topic modelling and knowledge graph revealed six latent research topics with nuanced patterns, highlighting a growing concentration on automated financial crimes that are distinctive from human-centric social engineering. The results also revealed the dual-use nature of GenAI in both facilitating and preventing financial crimes. On one hand, GenAI has been widely misused in financial cybercrimes such as algorithmic fraud, deepfake attacks, and smart contract exploitation. On the other hand, GenAI has enhanced crime prevention capacities, such as detection capabilities and vulnerability screening. Conclusions While GenAI facilitates various criminal opportunities for financial crime, it also provides insight into effective crime prevention strategies. This study demonstrated that research is increasingly focused on the technical and adversarial dimensions of the dual-use nature of GenAI, outlining a structural distinction between human-centric social engineering and automated financial crimes. The findings shed light on the importance of recognising the evolutionary landscape of financial crimes enabled by GenAI and the significance of embracing forward-looking governance frameworks for regulatory compliance in decentralised financial (DeFi) systems.

As generative artificial intelligence (GenAI) becomes increasingly embedded in education systems worldwide, urgent questions arise concerning whose knowledge these technologies elevate and whose they marginalize. This study adopts a twofold critical–constructive approach to examine GenAI’s role in reproducing epistemic hierarchies and to advance pathways toward more equitable use in education. Using a critical constructive qualitative design, we first conducted zero-shot prompt testing with ChatGPT-4 Turbo and Gemini 1.5 models across contexts in the Global North and Global South. The models responses were documented in real time and analyzed through a critical interpretive lens to surface patterns associated with digital neocolonialism. The critical phase of the study identifies six interconnected dimensions through which GenAI sustains Western dominance in educational contexts: Western curriculum ideologies, cultural imperialism, pedagogical control, language marginalization, racial and ethnic underrepresentation, and access inequity. For instance, when Gemini was asked to identify the seasons in the United States and Ghana, it returned the same four-season framework for both contexts, reflecting Western climatological assumptions. Across other prompts, GenAI outputs relied on stereotypical imagery, assumed Western-centered instructional resources, limited Indigenous and local language support, and disproportionately represented Western racial identities. In addition, subscription-based pricing models create structural barriers, as educators and institutions in much of the Global South face disproportionate costs due to currency differences. Building directly on these findings, the constructive phase advances two mitigation pathways for equitable GenAI in education. The first pathway targets AI design, emphasizing liberatory design methods, foresight by design, and the decentralization of GenAI development to strengthen local participation and data sovereignty. The second operates at the pedagogical level, advancing a human-centric prompt engineering model that empowers educators to contextualize prompts, critically interrogate outputs, and exercise pedagogical agency. These pathways position GenAI not merely as a technological tool, but as a site of ethical, and culturally responsive education.

In this paper, we investigated the role of generative AI in education in academic publications extracted from Web of Science (3506 records; 2019–2024). The proposed methodology included three main streams: (1) Monthly analysis trends; top-ranking research areas, keywords and universities; frequency of keywords over time; a keyword co-occurrence map; collaboration networks; and a Sankey diagram illustrating the relationship between AI-related terms, publication years and research areas; (2) Sentiment analysis using a custom list of words, VADER and TextBlob; (3) Topic modeling using Latent Dirichlet Allocation (LDA). Terms such as “artificial intelligence” and “generative artificial intelligence” were predominant, but they diverged and evolved over time. By 2024, AI applications had branched into specialized fields, including education and educational research, computer science, engineering, psychology, medical informatics, healthcare sciences, general medicine and surgery. The sentiment analysis reveals a growing optimism in academic publications regarding generative AI in education, with a steady increase in positive sentiment from 2023 to 2024, while maintaining a predominantly neutral tone. Five main topics were derived from AI applications in education, based on an analysis of the most relevant terms extracted by LDA: (1) Gen-AI’s impact in education and research; (2) ChatGPT as a tool for university students and teachers; (3) Large language models (LLMs) and prompting in computing education; (4) Applications of ChatGPT in patient education; (5) ChatGPT’s performance in medical examinations. The research identified several emerging topics: discipline-specific application of LLMs, multimodal gen-AI, personalized learning, AI as a peer or tutor and cross-cultural and multilingual tools aimed at developing culturally relevant educational content and supporting the teaching of lesser-known languages. Further, gamification with generative AI involves designing interactive storytelling and adaptive educational games to enhance engagement and hybrid human–AI classrooms explore co-teaching dynamics, teacher–student relationships and the impact on classroom authority.

Homecoming, more traditionally known as Mudik, has become a trending topic on several social media platforms as soon as the 11-day homecoming ritual ban was announced on 7 April 2021. Opinions, varying from those in favor of and against the ban, start to rapidly appear. Twitter, a social media platform which is now considered to be an extension of oneself and often used to express ones’ opinion, has become flooded with comments on the homecoming ritual ban. The swarm of opinions in the form of tweets were then used as a dataset for sentiment analysis in order to understand how people perceive the ban. The algorithm used in this research is the classification algorithm using the Support Vector Machine method. The dataset was classified into three sentiments: positive, negative, and neutral. The use of the Support Vector Machine algorithm yielded a 62% accuracy with this dataset. The sentiment analysis showed that the keyword "mudik" had a neutral sentiment for the most part. Meanwhile, results of engagement analysis show that the largest forms of engagements were retweets and liking tweets that had a neutral sentiment. When the neutral sentiment was removed, we found that the largest sentiment on the homecoming ritual ban was negative. This is likely due to the release of an addendum to the Covid-19 Handling Task Force Circular Number 13 of 2021 on 22 April 2021 that imposes more restrictions on and extends the effective dates of the restrictions related to the homecoming ritual ban; exactly one day before the data scraping of 5000 datasets on tweets from 23 April 2021 was carried out. The researcher had already sampled the tweets with the most engagements (those with the most retweets and likes). It was found that some tweets had a negative sentiment, but the model classified it as having a neutral sentiment. This may be affected by inaccuracies of dataset training as some of the tweets were in Malay rather than Indonesian. A challenge that needs to be overcome is the limited number of datasets for NLP training or sentiment analysis for the Indonesian language in comparison to that of the English language. On the other hand, this has become an opportunity for the researcher to develop a more appropriate training model.

Generative AI (GenAI) is a disruptive technology likely to generate a major impact on faculty and learners in medical education. This work aims to measure the perception of GenAI among medical educators and to gain insights into its major advantages and concerns in medical education. A survey invitation was distributed to medical education faculty of colleges of allopathic and osteopathic medicine within a single university during the fall of 2023. The survey comprised 12 items, among those assessing the role of GenAI for students and educators, the need to modify teaching approaches, GenAI's perceived advantages, applications of GenAI in the educational context, and the concerns, challenges, and trustworthiness associated with GenAI. Responses were obtained from 48 faculty. They showed a positive attitude toward GenAI and disagreed on GenAI having a very negative effect on either the students' or faculty's educational experience. Eighty-five percent of our medical schools' faculty responded to had heard about GenAI, while 42% had not used it at all. Generating text (33%), automating repetitive tasks (19%), and creating multimedia content (17%) were some of the common utilizations of GenAI by school faculty. The majority agreed that GenAI is likely to change its role as an educator. A perceived advantage of GenAI in conducting more effective background research was reported by 54% of faculty. The greatest perceived strengths of GenAI were the ability to conduct more efficient research, task automation, and increased content accessibility. The faculty's major concerns were cheating in home assignments in assessment (97%), tendency for blunder and false information (95%), lack of context (86%), and removal of human interaction in important feedback processes (83%). The majority of the faculty agrees on the lack of guidelines for safe use of GenAI from both a governmental and an institutional policy. The main perceived challenges were cheating, the tendency of GenAI to make errors, and privacy concerns.The faculty recognized the potential impact of GenAI in medical education. Careful deliberation of the pros and cons of GenAI is needed for its effective integration into medical education. There is general agreement that plagiarism and lack of regulations are two major areas of concern. Consensus-based guidelines at the institutional and/or national level need to start to be implemented to govern the appropriate use of GenAI while maintaining ethics and transparency. Faculty responses reflect an optimistic and favorable outlook on GenAI's impact on student learning.

Nowadays, educational institutes and universities are showing interest to improve quality of education system by monitoring teacher’s teaching, student’s learning, and course analysis using feedbacks. Teaching–learning process of outcome-based education requires the maximum involvement of students, teachers, and other stakeholders to identify and evaluate different aspects of education. In today’s digitized world, a huge amount of opinions are expressed daily on teaching-related topics using different social media platforms. Posted statements from students and teachers can provide a potential source for evaluating the teaching–learning process. The management of this huge content is again a cumbersome and time-consuming job if it is done manually. It is also very difficult to extract opinions about different aspects of the written unstructured text. A huge amount of rationale and context are expressed daily on the social media platform. Sentiment analysis is a majorly used technique in finding the sentiment from the unstructured text. Sentimental analysis of online social media is related to minimizing the traditional way of collecting suggestion and feedback. Most of the work has been done to process user comments which are only to classify the positive or negative sentiment using lexicon-based or machine learning methods at document level. It is found that sentimental analysis is a largely underused tool in the educational context to find opinions on different aspects. In this paper, we have done aspect-based sentiment analysis using machine learning- and lexicon-based approaches.

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).