Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election

Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This paper proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election

Sentiment Analysis Regarding Candidate Presidential 2024 Using Support Vector Machine Backpropagation Based

This research has the potential to make an important contribution to the development of computationally-based sentiment analysis, particularly in the political context. Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto, three candidates for the presidency of Indonesia, are examined using a Backpropagation-based Support Vector Machine (SVM) methodology in this study. This approach is used to categorize emotions into three groups: neutral, adverse, and favorable. Between July 1 and July 30, 2023, data on tweets mentioning the three presidential contenders was gathered. After processing the data, SVM was used while lowering the backpropagation process. The study's findings demonstrate that the performance of the model in determining public sentiment is greatly enhanced by the application of backpropagation-based SVM techniques. For each presidential contender, the evaluation was conducted using the f1 score, recall, and precision metrics. The evaluation's findings indicate that while the model struggles to distinguish between favorable and negative feelings toward particular presidential contenders, it performs better when categorizing neutral feelings. The SVM model is more accurately able to identify popular sentiment toward the three presidential candidates when the backpropagation approach is used. The results of the sentiment analysis are also represented by word clouds for each presidential contender, giving an intuitive sense of the words that are frequently used in public discourse. This study sheds light on the possibilities of using Twitter data to analyze political sentiment using the backpropagation-based SVM algorithm.

Explaining Data Patterns using Knowledge from the Web of Data

Knowledge Discovery (KD) is a long-tradition field aiming at developing methodologies to detect hidden patterns and regularities in large datasets, using techniques from a wide range of domains, such as statistics, machine learning, pattern recognition or data visualisation. In most real world contexts, the interpretation and explanation of the discovered patterns is left to human experts, whose work is to use their background knowledge to analyse, refine and make the patterns understandable for the intended purpose. Explaining patterns is therefore an intensive and time-consuming process, where parts of the knowledge can remain unrevealed, especially when the experts lack some of the required background knowledge. In this thesis, we investigate the hypothesis that such interpretation process can be facilitated by introducing background knowledge from the Web of (Linked) Data. In the last decade, many areas started publishing and sharing their domain-specific knowledge in the form of structured data, with the objective of encouraging information sharing, reuse and discovery. With a constantly increasing amount of shared and connected knowledge, we thus assume that the process of explaining patterns can become easier, faster, and more automated. To demonstrate this, we developed Dedalo, a framework that automatically provides explanations to patterns of data using the background knowledge extracted from the Web of Data. We studied the elements required for a piece of information to be considered an explanation, identified the best strategies to automatically find the right piece of information in the Web of Data, and designed a process able to produce explanations to a given pattern using the background knowledge autonomously collected from the Web of Data. The final evaluation of Dedalo involved users within an empirical study based on a real-world scenario. We demonstrated that the explanation process is complex when not being familiar with the domain of usage, but also that this can be considerably simplified when using the Web of Data as a source of background knowledge

Twitter Sentiment Analysis: Application for Classifying Tweets with Video Games as Keywords

The growth of microblogging services has expanded exponentially in recent years for mining user opinions. Sentiment analysis was applied to classify Twitter posts with video game titles as keywords. An analysis of the blog history, words and sentiments associated with the blog can help reveal whether the particular game is ‘violent’ and stress inducing or ‘non-violent’ and benign. An application was developed to collect and clean data. Naïve Bayes algorithm was applied to the cleaned data to determine the polarity of the words on the data to come to a conclusion whether, based on the words of the tweet, the particular game could be classified as ‘violent’ or ‘non-violent’. The results of the algorithm are analysed for accuracy, precision and recall. Deep learning models are discussed for use in future to improve accuracy

Instrumentalisation of the environment and the diversionary behavior in non-conflictual conditions

Comme la protection de l'environnement reçoit une attention croissante dans le discours politique actuel, il importe de se demander si ce phénomène représente une politique d'état bien définie ou un simple instrument stratégique. Ce mémoire cherche à répondre à cette question, en utilisant la théorie de la diversion conflictuelle. Cette théorie affirme que les chefs politiques éprouvant des problèmes internes appliquent une politique étrangère agressive contre les états non démocratiques. Ce comportement étatique est expliqué en se limitant à l'analyse des périodes de conflit, laissant ainsi un grand manque dans la recherche qui porte sur la diversion étatique pendant les périodes de non-conflit. Cette étude vise donc à déterminer si les instabilités internes peuvent être associées aux comportements de diversion étatique, et ce, en période non conflictuelle. De plus, cette recherche vise à démontrer que les valeurs écologiques sont utilisées comme instruments de politique étrangère. Pour ce faire, les discours politiques entre les États-Unis et la Chine de 1979 à 2004 sont analysés, en utilisant un modèle qualitatif et comparatif. L'hypothèse centrale prévoit que, durant les périodes de non-conflit, les États-Unis utilisent l'environnement pour attaquer la Chine. Ce comportement est surtout observé lorsque l'administration américaine souffre de troubles internes et lorsque l'opinion publique américaine est en faveur de la protection de l'environnement

The Emergent Logic of Health Law

The American health care system is on a glide path toward ruin. Health spending has become the fiscal equivalent of global warming, and the number of uninsured Americans is approaching fifty million. Can law help to divert our country from this path? There are reasons for deep skepticism. Law governs the provision and financing of medical care in fragmented and incoherent fashion. Commentators from diverse perspectives bemoan this chaos, casting it as an obstacle to change. I contend in this Article that pessimism about health law’s prospects is unjustified, but that a new understanding of health law’s disarray is urgently needed to guide reform. My core proposition is that the law of health care provision is best understood as an emergent system. Its contradictions and dysfunctions cannot be repaired by some master design. No one actor has a grand overview—or the power to impose a unifying vision. Countless market players, public planners, and legal and regulatory decisionmakers interact in oft-chaotic ways, clashing with, reinforcing, and adjusting to each other. Out of these interactions, a larger scheme emerges—one that incorporates the health sphere’s competing interests and values. Change in this system, for worse and for better, arises from the interplay between its myriad actors. By quitting the quest for a single, master design, we can better focus our efforts on possibilities for legal and policy change. We can and should continuously survey the landscape of stakeholders and expectations with an eye toward potential launching points for evolutionary processes—processes that leverage current institutions and incentives. What we cannot do is plan or predict these evolutionary pathways in precise detail; the complexity of interactions among market and government actors precludes fine-grained foresight of this sort. But we can determine the general direction of needed change, identify seemingly intractable obstacles, and envision ways to diminish or finesse them over time. Dysfunctional legal doctrines, interest group expectations, consumers’ anxieties, and embedded institutional and cultural barriers can all be dealt with in this way, in iterative fashion. This Article sets out a strategy for doing so. To illustrate this strategy, I suggest emergent approaches to the most urgent challenges in health care policy and law—the crises of access, value, and cost