Sentiment analysis on Twitter data towards climate action

This study aims to analyze the sentiment of the Hindu community toward religious issues on social media, with a specific focus on Kapuas Regency, Central Kalimantan. The development of social media as a digital public sphere has positioned platforms such as Instagram and Twitter as primary spaces for the spontaneous and open expression of opinions, perceptions, and religious attitudes. This study is important because religious issues in digital spaces often influence social harmony and shape religious discourse within society. The research employs a quantitative approach using text mining and sentiment analysis based on Natural Language Processing (NLP). Primary data were collected through web scraping techniques, utilizing Apify for Instagram and asynchronous Python scripts for Twitter, with relevant keywords, hashtags, and geographic indicators. The analysis process includes text preprocessing (cleaning, tokenization, stopword removal, and stemming), followed by sentiment classification using a lexicon-based approach with the InSet dictionary into three categories: positive, negative, and neutral. The analysis results were evaluated using a confusion matrix, along with precision, recall, and F1-score metrics to assess model reliability. The findings indicate that positive sentiment predominates on both Instagram and Twitter, followed by neutral sentiment, while negative sentiment appears in only a small proportion. Positive sentiment is generally associated with expressions of prayer, gratitude, tolerance, and calls for togetherness, whereas negative sentiment tends to emerge in discussions related to ritual differences or responses to socio-religious controversies. The sentiment analysis model achieved an accuracy of 100% on Instagram data (self-evaluation) and 74.4% on Twitter data (manual evaluation), with relatively high precision and recall values, indicating that the results are statistically reliable.

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.

Just eight years from now, in 2030, the world will once again review our progress toward the United Nations Sustainable Development Goals (SDGs), a bold set of 17 interconnected goals with 169 targets, agreed upon by the 191 United Nations (UN) members, centered around creating a more equitable world for all. The SDGs were originally published in 2015 on the seventieth anniversary of the UN to set out a “supremely ambitious and transformational vision…a world free of poverty, hunger, disease and want, where all life can thrive” [1]. The SDGs were developed with a full recognition that humanity’s needs are deeply connected and not siloed. Despite this recognition, we continue to approach humanity’s needs in ways that belie the interconnectedness of those needs; we continue to work in ways that do not fully acknowledge that basic development needs such as housing, water, food, gender equality, food insecurity, poverty alleviation, and the environment are interconnected. We believe this is unacceptable because it fragments the discourse around the fullness of what humanity itself needs to thrive.The world of research is just as fragmented as the world of programs and policies around the SDGs. Development aid and government investments in sectoral programs are normally examined for their impacts on narrow outcomes. For example, few programs in nonhealth sectors are assessed for their influence on human health outcomes. While annual global development aid reached $161.2 billion in 2020 [2], economic and agricultural development programs rarely evaluate their health impacts [3, 4]. More importantly, most poverty alleviation and food security programs are not developed with improving health or health equity as a key objective, and as a result, improvements in health are not optimized with traditional development approaches. Additional research examining the linkage between poverty alleviation, improved food security, gender equality, planetary health, and human health could further encourage large global health and development actors to invest in more interdisciplinary approaches.Systemic racism as well as ethnic, gender, and other forms of discrimination negatively affect progress toward achieving the SDGs. Minoritized racial and ethnic groups are generally further behind advantaged racial and ethnic groups in poverty, economic growth, and access to clean water and food, all of which adversely affect health and well-being. Those who live in poverty typically have less access to food, less access to clean water and sanitation, and more exposure to pollution than those who have a higher income [5]. Physical and psychological health disparities are further exacerbated by racial disparities related to environmental injustice and climate change [6]. Despite a call by several UN human rights groups, the SDGs are silent on the eradication of systemic racism and racial, ethnic, and many other forms of discrimination, which constitute global barriers to human development and the fulfillment of human rights throughout the life span.Prior to the COVID-19 pandemic, many countries around the world had made substantial progress toward ending poverty in all its forms (SDG 1). While 36% of the world’s population lived in extreme poverty in 1990, by 2015 this had fallen to 10%. Due to the COVID-19 pandemic, however, it is estimated that an additional half a billion people have been pushed or pushed further into poverty [7]. The effects of infectious diseases like COVID-19 on economic outcomes serve as yet another illustration of the strong positive correlation established between health and wealth over decades of economics research. Moreover, a vibrant literature in economics has demonstrated a strong bidirectional relationship between health and economic outcomes in both low- and high-income countries [8]. This research has demonstrated that there are large returns to investing in health and that programs that protect or improve economic outcomes—such as social protection programs—result in significant health improvements as well. Moreover, in the field of economics, the past two decades have witnessed a revolution in scientific methods for learning how to end poverty and improve well-being in low- and middle-income countries. Randomized trials of interventions and policies have become far more common and have opened new possibilities for understanding how to achieve SDG 1, and also with important impacts on SDG 3 (“Good health and well-being”) [9]. However, most of the economic research on poverty alleviation and health in low- and middle-income countries typically appears in journals that are not widely read by health scientists or by stakeholders in the countries where the research takes place. Through the section on SDG 1, Advances in Global Health aims to provide new opportunities for dissemination of research on the linkages between poverty and health.Food insecurity and poor diets (SDG 2) compromise nutrition and health outcomes in profound ways. Global and national models suggest that malnutrition and poor diets are the leading contributors to the burden of disease around the world [10]. These impacts operate through multiple pathways. For instance, maternal and child undernutrition contribute to almost half of all child deaths each year in the developing world, and 11% of the global burden of disease, including noncommunicable disease, is linked to poor diets [11]. These poor health outcomes, in turn, can affect other SDGs through increased health expenditures, loss of wages when people are too sick to work, or care burdens that often fall disproportionately on women. The connections between food insecurity, diets, nutrition, and health are strong, mutually reinforcing, and rooted in social and economic inequities. Often, the intergenerational transmission of food insecurity and poor nutrition prevents families and communities from escaping these vicious cycles. And yet too much research on food security and nutrition is out of the view of those working on achieving health goals, because it sits in literatures in agriculture, food policy, nutrition, and economics, and lacks a transdisciplinary and trans-SDG emphasis.Gender issues and global health challenges are diverse, emanating out of a complex global context, including a multiplicity of social and cultural contexts. SDG 5 is committed to gender equality, with several targets geared toward ending discrimination against women and improving their opportunities, especially in economics, leadership, and reproductive health. Gender equality is important to advancing global health for several reasons [12, 13]. Without attention to gender equality, females will continue to suffer from reproductive health challenges such as high rates of teenage pregnancies, infertility, and maternal mortality and increased risk of sexually transmitted infections including HIV. Beyond reproductive health, gender matters in the field of gender-based violence, access to health-care services, gender-disaggregated data, and much more [14, 15, 16]. These, in turn, invite us to interrogate the gender-biased norms and gender responsiveness of health policies, financing, and the entire political economy. Engendered by different crises and challenges, these matters play out differently in unique cultural and social environments.Since the 1950s and 1960s, humans have been transforming and disrupting most of our planet’s natural systems at a much-accelerated pace, from the deep oceans to the upper atmosphere, causing loss of biodiversity, overexploitation of fisheries, rising carbon dioxide in the atmosphere, acidification of oceans, and loss of tropical forests. Those disruptions interact with each other in complex ways to alter the fundamental conditions for human health and well-being and, ultimately, affect nearly every dimension of human health [17]. Planetary health focuses on understanding and quantifying the human health impacts of these global environmental disruptions and on developing solutions that will allow humanity and the natural systems we depend on to thrive now and in the future [18]. Our vision is to encourage the preservation and sustainability of natural systems for the benefit of human health (SDG 3), aligned with SDGs 13 (Climate Action), 14 (Life below Water), and 15 (Life on Land) through the study and characterization of the inextricably linked dependencies between human health and the health of natural systems.Inequities in the representation of scientists from low- and middle-income countries as well as minority groups in most countries has plagued academic research. For example, in a systematic review of health research in sub-Saharan Africa between 2014 and 2016, 14% of papers had no local coauthors (i.e., from the country where the research took place), and representation of local coauthors as first author was also low (23%) when the research involved researchers from the United States, Canada, or Europe [19]. This is consistent with extensive global health literature [20, 21]. Even higher rates of nonrepresentation of local coauthors are reported in economics research that takes place in low- and middle-income countries. A recent review discovered that only 23% of first authors were from the country of the study’s focus [22, 23]. Research funding and publication fees contribute to such inequities and are some of the barriers that need to be addressed to improve equity in the recognition of research. We believe that supporting the SDGs cannot be separated from addressing these significant inequities in research dissemination.Advances in Global Health is a peer-reviewed, open-access research journal that is committed to decolonizing and democratizing global health. This entails purposefully giving prominence to knowledge and knowledge production from low- and middle-income countries as well as racial and ethnic minority communities within all countries. In so doing, Advances in Global Health is committed to the following: (1) having an editorial board that is representative of relevant disciplines and inclusive of scholars from low- and middle-income countries; (2) publishing knowledge from low- and middle-income countries; (3) focusing on issues that are pertinent to low- and middle-income countries; and (4) securing representation of lead authors from low- and middle-income countries. The journal is dedicated to transdisciplinarity and inclusivity; seeks to fully engage researchers from low- and middle-income countries in all aspects of the journal; and specifically encourages contributions led by authors situated in these regions. Furthermore, we have chosen to use an open-access format to ensure that the journal content is freely available to researchers, policy makers, and the general community free of charge. For at least through the end of 2023, publication is free of charge; in subsequent years, the publication charge will be reduced or free for authors from low- and middle-income countries.Thematically, Advances in Global Health is focused on advancing health equity throughout the world by publishing research aligned with the SDGs. Advances in Global Health centers health as its focus and promotes an interdisciplinary approach to studying the bidirectional relationship between health outcomes and other key sectors—for example, ending poverty and promoting food security, gender equality, and planetary health. These sections emphasize individual disciplinary approaches to the SDGs as outlined in each section’s aims and scope, forming the core areas of research inquiry and editorial activity of the journal (Figure 1). However, the journal specifically welcomes and encourages research that is interdisciplinary or transdisciplinary in nature and that cuts across multiple SDGs, to avoid research fragmentation and generate a commonplace for integrative science. Finally, as an underlying theme of the journal connected with each of the sections, we will promote scholarly activity that addresses racism and other forms of discrimination, including the rights of Indigenous people.In summary, the Editorial Board, UC Press, and the UC Global Health Institute are firmly committed to having Advances in Global Health be a leading academic journal to share cutting-edge transdisciplinary research and commentaries aiming at improving health equity worldwide. Advances’ editorial policies will actively address research publishing inequities in low- and middle-income countries to effectively promote free sharing of knowledge around the globe.AGL is sponsored by Emerge, the Emerging Diseases Epidemiology Research Training grant D43 TW007393 awarded by the Fogarty International Center of the US National Institutes of Health.The authors declare that they have no competing interests.

This research analyzes the sentiment of Twitter users regarding tourism in Indonesia using the keyword "wonderful Indonesia" as the tourism promotion identity. The aim of this study is to gain a deeper understanding of the public sentiment towards "wonderful Indonesia" through social media data analysis. The novelty obtained provides new insights into valuable information about Indonesian tourism for the government and relevant stakeholders in promoting Indonesian tourism and enhancing tourist experiences. The method used is tweet analysis and classification using the K-nearest neighbor (KNN) algorithm to determine the positive, neutral, or negative sentiment of the tweets. The classification results show that the majority of tweets (65.1% out of a total of 14,189 tweets) have a neutral sentiment, indicating that most tweets with the "wonderful Indonesia" tagline are related to advertising or promoting Indonesian tourism. However, the percentage of tweets with positive sentiment (33.8%) is higher than those with negative sentiment (1.1%). This study also achieved training results with an accuracy rate of 98.5%, precision of 97.6%, recall of 98.5%, and F1-score of 98.1%. However, reassessment is needed in the future as Twitter users' sentiment can change along with the development of Indonesian tourism itself.

Sentiment Analysis Applied to Digital Conversations Among Warm Autoimmune Hemolytic Anemia (wAIHA) Patients Receiving Rituximab and/or Blood Transfusion

IntroductionAlthough global food production has tripled, sustainable agriculture remains threatened by climate change, land degradation, and financial gaps, especially in smallholder communities. This study introduces the Integrated Waqf Environment Model (IWEM) as a waqf-based funding mechanism to support sustainable agriculture. Additionally, sentiment analysis using VADER Python is employed to assess public perceptions of sustainable agriculture.ObjectivesThis research aims to measure public sentiment regarding sustainable agriculture on the Twitter social media platform. Additionally, the study proposes the Integrated Waqf Environment Model (IWEM) for sustainable agriculture funding as a response to the challenges faced in the agricultural sector.MethodThe research methodology involves the extraction and classification of tweet data using the Python library called VADER (Valence Aware Dictionary and Sentiment Reasoner). The study utilizes tweet data posted within the last year. The research findings indicate fluctuations and a decrease in the number of tweets discussing sustainable agriculture. The location with the highest tweet activity related to sustainable agriculture is Brussels, Belgium, with 642 tweets during the observation period.ResultsWord cloud analysis on keywords reveals that, in positive sentiment, terms like "food security" and "climate change" dominate the visualization. On the other hand, in negative sentiment, words like "farmer" and "private farmland" appear more frequently. Overall, the majority of tweets express a positive attitude toward sustainable agriculture, with 68.5% positive sentiment. Approximately 22.3% of tweets indicate neutral sentiment, without a strong positive or negative tendency. Only 9.1% of tweets contain negative sentiment, suggesting that a small portion expresses less supportive views of sustainable agriculture.ImplicationsThe research provides insights into public sentiment on sustainable agriculture and proposes IWEM as a sustainable financing solution. IWEM can bridge financial gaps, enhance technological adoption, and support policy development for sustainable agriculture. This model aligns with the SDGs, ensuring long-term agricultural resilience and environmental conservation.Originality/NoveltyThis study uniquely integrates Islamic social finance with Smart Agriculture 5.0 through IWEM. It also employs sentiment analysis to understand public perceptions, offering data-driven insights for policymakers. The research contributes a novel waqf-based framework that can be scaled for sustainable agricultural development.

Objective: To make an extensive analysis of sentiment within the discourse surrounding COVID-19 vaccines on Twitter, employing Natural Language Processing (NLP) methodologies. Method: The research methodology encompasses data collection via the Twitter API, followed by sentiment analysis facilitated by the TextBlob library. Pre-processing stages are integrated to cleanse and standardize the Twitter data. Subsequently, sentiment analysis categorizes tweets into positive, negative, and neutral sentiments based on polarity scores. Findings: The findings, grounded in a dataset spanning from March 1, 2022, to April 30, 2022, comprising 61,934 tweets, unveil that 45.0% of tweets conveyed positive sentiment, 17.3% exhibited negativity, and 37.7% maintained neutrality. Moreover, an exploration of tweet subjectivity revealed that 70.1% of the content expressed subjectivity, while 29.9% conveyed objectivity. The research is augmented with visual representations, including word clouds and subjectivity-polarity graphs, that offer a more intuitive understanding of sentiment trends. Novelty: This study contributes to the expanding landscape of sentiment analysis and its application within the context of public health crises, empowering stakeholders with valuable knowledge to enhance vaccine acceptance and effectiveness. The tool used “Tweet Downloader” in data collection makes this study different from other reviewed studies. Keywords: COVID-19 vaccine, Twitter sentiment analysis, Public perception, Natural Language Processing (NLP), Social media data

This study explores how Artificial Intelligence (AI), particularly Natural Language Processing (NLP) and machine learning, can be leveraged to analyze public discourse on climate change and improve climate communication strategies. Focusing on four key questions, the research examines how AI classifies public sentiment, identifies dominant topics, detects framing structures, and generates actionable insights to inform targeted communication. Over one million climate-related posts were collected from Twitter and Facebook between January and June 2024. Sentiment analysis using a fine-tuned BERT model categorized posts into positive, negative, and neutral sentiments, while Latent Dirichlet Allocation (LDA) revealed key topics. Framing analysis employed supervised machine learning to classify posts into narrative frames, and AI-powered visualization tools were used for interpretation. The results indicate a polarized sentiment distribution: 45% negative, 35% positive, and 20% neutral. Negative posts centered on skepticism and political inaction, while positive posts supported renewable energy and activism. Thematic analysis highlighted five key topics: scientific evidence, activism, economic implications, political debate, and environmental justice. Framing analysis revealed four dominant frames—climate urgency, economic impact, political action, and environmental justice—that shape public perception. Temporal sentiment shifts aligned with major events, suggesting public discourse is responsive to political and activist developments. This research demonstrates the potential of AI to provide scalable, data-driven insights into public climate discourse. By integrating these insights into strategic planning, communicators can design more effective, emotionally resonant messages, enhancing public engagement and supporting collective climate action.

Due to the noise and informal language present in Twitter data, it is difficult to perform sentiment analysis on the platform. In recent years, a number of transformer models have been developed that can perform well in this type of task. This study aims to analyze the performance of these models on Twitter data. The study utilizes a publicly-available dataset of tweets with neutral, positive, or negative sentiment. It preprocesses the data and tokenizes it using WordPiece. Three transformer models are then tuned using the labeled tweets' pre-defined weights and the models' training weights from large language modeling projects. The models are trained on a 5-phased scale. The three models' performance was evaluated using various metrics, such as accuracy, recall, and F1 score. The results show that the models performed well overall, with ELECTRA leading the way with an accuracy of 85.8%, followed by XLNet and BERT with 84.3% and 84.5% accuracy, respectively. The study also looked into the hyperparameters' impact on the performance. It revealed that batch sizes and learning rates have a significant effect on the models' performance. The results indicate that the models performed better with larger batch sizes and lower learning rates. The study concluded that the three pre-trained transformer models, namely XLNet, ELECTRA, and BERT, were able to perform well in terms of their performance when it came to analyzing Twitter data. Their findings can be beneficial for those working in the field of sentiment analysis on social media platforms.

Can goals change the world? In September 2015, the United Nations (UN) unanimously adopted the 17 Sustainable Development Goals (SDGs) to "transform our world" by 2030. With these highly ambitious goals, world leaders pledged to "end hunger", "achieve gender equality", "protect life on land", and much more. Importantly, the SDGs emphasise the need for more collaboration, the breakdown of 'silos' in global governance, and to achieve the SDGs in a balanced and integrated manner. This call for both institutional and policy integration in global governance is long-standing, but remains highly challenging. The SDGs - as the most central and ambitious global agenda so far - have given a renewed impetus to this call. Yet, empirically, we know very little about the effects of global goals. Can non-binding, highly ambitious goals really steer global governance? In this thesis, I investigate the steering effects of the SDGs on international organisations. Specifically, I assess whether - since the advent of the SDGs - institutional integration among and policy integration within international organisations has increased. My methods are both quantitative and qualitative. I rely primarily on a large dataset of scraped websites of 276 international organisations, and assess the contents of and hyperlinks between websites. To complement this data, I also conduct a discourse analysis and analysis on Twitter data. My findings can be summarised in four key points. First, while many international organisations use the SDGs on their websites, the SDGs are not yet a fully global framework. Many international organisations use the SDGs on their websites, and this increases over time. However, the SDGs are mainly used by the larger organisations and by UN entities, while many of the smaller and more regional organisations do not or barely use the SDGs. Second, policy integration overall is increasing, but the integration of environmental topics with socio-economic topics is lagging behind. As mentioned, the call for more policy integration is long-standing, and indeed an increase in policy integration is observed in my study. However, mainly socio-economic topics (ending poverty, decent work, innovation and infrastructure) are integrated with one another, while the integration of environmental with socio-economic topics occurs much less frequent. Third, while policy integration is overall increasing, the SDGs appear not to be an influencing factor. The international organisations that use the SDGs more, do not subsequently show an increase in policy integration. It is rather the other way around: those international organisations that already showed higher levels of policy integration, also use the SDGs more. Thus, the SDGs are used to frame efforts towards policy integration, but do not spur it. Fourth, the SDGs facilitate institutional integration within policy domains. This also means, conversely, that institutional integration between policy domains has decreased. Thus, contrary to political expectations of the SDGS to "break down silos", silos around the 17 issue areas of the SDGs and around the economic, social and environmental dimension of sustainability have strengthened.

<p>The Sustainable Development Goals (SDG) are a set of 17 goals that was released by the United Nations (UN) in 2015. Each goal has a target figure that countries and, ideally, the world should aim to reach in order to create sustainability within that sector for current and future generations. Seven years after the SDGs were released, thousands of studies and academic articles have promoted the SDGs, as well as regular updates that have been released by the UN on goal progress specific to each country. In addition, multiple studies have highlighted synergies and tradeoffs between SDGs that have the potential to significantly influence goal completion (Biggeri et. Al, 2019; Moyer & Bohl, 2019; Jose-Serrano, 2022; Zhao et. al, 2021). With this information in mind, this study aims to conduct a large-scale network analysis of research articles concerning SDG progress to answer the following questions: Which SDGs receive the most attention from researchers? What are the perceptions in academia regarding the synergies/ trade-offs between the SDGs? The network analysis will be conducted using the search engine SCOPUS resulting in hundreds of retrieved papers for each category within the SDGs. Results from this study will be compared to current SDG progress and known synergies and tradeoffs within the SDGs in order to determine how the perception of the SDGs compare with research conclusions and known SDG goal progress. This information will serve as an indication of which goals, synergies, or tradeoffs researchers and industries are aware of and readily researching and which of these categories needs more attention within academic circles. The ultimate goal for this research is that the results can be used as a tool to advocate for what SDG research is most needed in order for SDG goals to reach completion by 2030.</p>

In this paper, the focus is on sentiment analysis of web 3.0 enabled twitter dataset. The objective of the project is to explore various methods for performing sentiment analysis on Twitter datasets and implementing these methods on the web3.0 Twitter platform. The project involves collecting Twitter data through blockchain-based applications, preprocessing the data to remove noise, and applying machine learning models for sentiment analysis. Sentiment analysis is simply the extraction of thoughts, ideas, opinions, and emotions from sources such as text, speech, tweets, and databases using natural language processing (NLP) This process involves text segmentation mentally makes it "good," "bad," and "neutral" groups. In addition, it is known by other terms such as objective evaluation, mindfulness mining, and rating extraction. Web 3.0, also known as Web3, represents the third contemplated iteration of the World Wide Web, which aspires to establish a connected, transparent and intelligent online environment Based on the concept of decentralization, blockchain technology and the implementation of token-based economies. The main outcome of the project is to gain insights into the sentiment of users by analyzing WEB3.0 enabled Twitter data. By implementing sentiment analysis techniques on a WEB3.0 enabled Twitter dataset, the project aims to contribute to the field of sentiment analysis and showcase the effectiveness of using WEB3.0 enabled for data collection and analysis. The project aims to provide valuable insights for various methods of sentiment analysis for researchers. Keywords – Sentiment Analysis, Blockchain- Enabled, Twitter data, WEB 3.0, Machine Learning Models, User Sentiment, Research Contribution

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.

The World Health Organization recognizes that physical activity (PA) during childhood is crucial for healthy development, aligning well with the achievement of several United Nations (UN) Sustainable Development Goals (SDGs). This study aimed to explore the associations between 10 key indicators of PA for children and adolescents assessed in the Global Matrix 4.0 project, and the UN SDGs. Data from 57 countries/jurisdictions of the Global Matrix 4.0 project were used. The UN SDG indicators were sourced from the SDG Transformation Center, which publishes each country's performance on each of the 17 SDGs. Given the robust evidence supporting plausible links between PA and SDGs 3 (good health and well-being), 9 (industry, innovation, and infrastructure), 11 (sustainable cities and communities), 13 (climate action), and 16 (peace, justice, and strong institutions), these SDGs were investigated. Countries/jurisdictions with good and moderate performance in achieving SDG 3, SDG 9, SDG 11, and SDG 16 had higher grades than countries/jurisdictions with fair performance in achieving these SDGs for the following indicators: Organized Sports and PA, Community and Environment, and Government Investments and Strategies. However, countries/jurisdictions with good performance in achieving SDG 13 had lower grades than countries/jurisdictions with fair performance in achieving SDG 13 for the following indicators: Organized Sports and PA, Community and Environment, and Government Investments and Strategies. Organized Sports and PA, Community and Environment, and Government Investments and Strategies were the indicators that demonstrated differences between countries/jurisdictions with good and poor performance in achieving the SDGs.

Organisations these days are actively using social media platforms to engage with potential and existing customers and monitor what they say about the organisation’s product or service. The most important area within social media monitoring lies in how to gain insight for sentiment analysis. Sentiment analysis helps in effective evaluation of customer’s sentiments in real time and takes on a special meaning in the context of online social networks like Twitter and Facebook, which collectively represent the largest online forum available for public opinion. Sentiment Analysis is not about retrieving and analyzing the analytics purely on the basis of positive, negative or neutral sentiment. It is imperative to assess the influencers of the sentiments in terms of Retweet and Share option used by them on Twitter and Facebook platform respectively. Measuring the intensity is other important aspect of sentiment analysis process. What kind of nouns, adjectives, verbs and adverbs are used in the opinion across the Twitter and Facebook platform matters as well since it exhibits the intensity of the underlying emotion in the text written. This study was conducted to propose a framework to identify and analyse the positive and negative sentiments present in Twitter and Facebook platforms and an algorithm was prepared to measure the intensity and influence of the positive, negative sentiment in particular using the document and sentence level analysis technique.