The outbreak of COVID-19 is one of the major pandemics faced by the world ever and the World Health Organization (WHO) had declared it as the deadliest virus outbreak in recent times. Due to its incubation period, predicting or identifying the paints had become a tough job and thus, the impact is on a large scale. Most of the countries were affected with Coronavirus since December 2019 and the spread is still counting. Irrespective of the preventive measures being promoted on various media, still the speculations and rumors about this outbreak are peaks, that too particular with the social media platforms like Facebook and Twitter. Millions of posts or tweets are being posted on social media via various apps and due to this, the accuracy of news has become unpredictable, and further, it has increased panic among the people. To overcome these issues, a clear classification or categorization of the posts or tweets should be done to identify the accuracy of the news and this can be done by using the basic sentiment analysis technique of data sciences and machine learning. In this project, Twitter will be considered as the social media platform and the millions of tweets will be analyzed for aspect mining to categorize them into positive, negative, and neutral tweets using the NLP techniques. SVM and Naive Bayes approach of machine learning and this model will be developed.

Indiana University-Purdue University Indianapolis (IUPUI) / Software requirements, for complex projects, often contain specifications of non-functional attributes (e.g., security-related features). The process of analyzing such requirements for standards compliance is laborious and error prone. Due to the inherent free-flowing nature of software requirements, it is tempting to apply Natural Language Processing (NLP) and Machine Learning (ML) based techniques for analyzing these documents. In this thesis, we propose a novel semi-automatic methodology that assesses the security requirements of the software system with respect to completeness and ambiguity, creating a bridge between the requirements documents and being in compliance. Security standards, e.g., those introduced by the ISO and OWASP, are compared against annotated software project documents for textual entailment relationships (NLP), and the results are used to train a neural network model (ML) for classifying security-based requirements. Hence, this approach aims to identify the appropriate structures that underlie software requirements documents. Once such structures are formalized and empirically validated, they will provide guidelines to software organizations for generating comprehensive and unambiguous requirements specification documents as related to security-oriented features. The proposed solution will assist organizations during the early phases of developing secure software and reduce overall development effort and costs.

In today’s digital era, text data plays a ubiquitous role across various domains. This bachelor thesis focuses on the field of sentiment analysis, specifically addressing the task of classifying text into positive, negative, or neutral sentiments with the help of an AI tool. The key research questions addressed are: (1) How can an accurate sentiment classification system be developed to categorize customer reviews as positive, negative, or neutral? (2) How can the performance of the sentiment analysis tool be optimized and evaluated, considering the factors that influence its accuracy? (3) How does Chat-GPT evaluate text-based feedback from customers with our results as input, i.a. can"Artificial General Intelligence" be adapted to solve a specific problem in the domain of this work? To accomplish this, the study harnesses the power of RoBERTa, an implemented transformer model renowned for its prowess in natural language processing tasks. The model will mainly focus on review comments from Amazon and on the product, "Samsung Galaxy A53". A small comparative analysis will also be carried out with Chat-GPT and the RoBERTa model’s sentiment positions. The results demonstrate the effectiveness of the RoBERTa model in sentiment classification, showcasing its ability to categorize sentiments for different review comments. The evaluation process identified key factors that influence the tool’s performance and provided insights into areas for further improvement. In conclusion, this thesis contributes to the field of sentiment analysis by providing a comprehensive overview of the development, optimization, and evaluation of an AI-powered text analysis tool for the sentiment classification of customer reviews. The result affects the importance of understanding customer sentiment and providing practical implications for businesses to improve their decision-making processes and enhance customer satisfaction.

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We analyze globally normalized transition-based neural network models for dependency parsing on English, German, Spanish, and Catalan. We compare the results with FreeLing, an open source language analysis tool developed at the UPC natural language processing research group. Furthermore we study how the mini-batch size, the number of units in the hidden layers and the beam width affect the performances of the network. Finally we propose a multi-lingual parser with parameters sharing and experiment with German and English obtaining a significant accuracy improvement upon the monolingual parsers. These multi-lingual parsers can be used for low-resource languages of for all the applications with low memory requirements, where having one model per language in intractable.

As new forms of propaganda and information control spread across the internet, the need for novel ways of identifying them increases as well. One increasingly popular method of spreading false messages on microblogs like Twitter is to disseminate them from seemingly ordinary, but centrally controlled and coordinated user accounts – sockpuppets. In this paper we examine a number of potential methods for identifying these by way of applying plagiarism detection algorithms for text, and evaluate their performance against this type of threat. We identify one type of algorithm in particular – that using vector space modeling of text – as particularly useful in this regard. / Allteftersom  nya  former  av  propaganda  och  informationskontroll  sprider sig över internet krävs också nya sätt att identifiera dessa. En  allt mer populär metod för att sprida falsk information på mikrobloggar  som  Twitter  är  att  göra  det  från  till  synes  ordinära,  men  centralt  kontrollerade och koordinerade användarkonton – på engelska kända  som “sockpuppets”. I denna undersökning testar vi ett antal potentiella  metoder  för  att  identifiera  dessa  genom  att  applicera  plagiatkontrollalgoritmer  ämnade  för  text,  och  utvärderar  deras prestanda mot denna sortens hot. Vi identifierar framför allt en typ av  algoritm  –  den  som  nyttjar  vektorrymdsmodellering  av  text  –  som speciellt användbar i detta avseende.

Evaluation of student understanding of learning material is critical to effective teaching. Current computer-aided evaluation tools exist, such as Computer Adaptive Testing (CAT); however, they require expert knowledge to implement and update. We propose a novel task, to create an evaluation tool that can predict student performance (knowledge) based on general performance on test questions without expert curation of the questions or expert understanding of the evaluation tool. We implement two methods for creating such a tool, find both methods lacking, and urge further investigation.

Elite military selection programs like the 75th Ranger Regiment's Ranger Assessment and Selection Program (RASP) are known for their difficulty and high attrition rates, despite substantial candidate screening just to get into such programs. The current study analyzes Ranger candidates 'attitudes, attributions, and goals (AAGs) and their relationship with successful completion of pre-RASP, a preparation phase for the demanding eight-week RASP program. Candidates' entry and exit surveys were analyzed using natural language processing (NLP), as well as more traditional statistical analyses of Likert-measured survey items to determine what reasons for joining and what individual goals related to candidate success. Candidates' Intrinsic Motivations and Satisfaction as measured on entry surveys were the strongest predictors of success. Specifically, candidates' desire to deploy or serve in combat, and the goal of earning credibility in the Rangers were the most important reasons and goals provided through candidates' open-text responses. Additionally, between-groups analyses between Black Candidates, Hispanic Candidates, and White Candidates showed that differences in candidate abilities and motivations better explains pre-RASP attrition than demographic proxies such as race or ethnicity. The study's use of NLP demonstrates the practical utility of applying machine learning to quantitatively analyze open-text responses that have traditionally been limited to qualitative analysis or subject to human coding, although predictive models utilizing more traditional Likert-measurement of AAGs had better predictive accuracy. / Doctor of Philosophy / Elite military selection programs like the 75th Ranger Regiment's Ranger Assessment and Selection Program (RASP) are known for their difficulty and high attrition rates, despite substantial candidate screening just to get into such programs. The current study analyzes Ranger candidates' attitudes and goals and their relationship with successful completion of pre-RASP, a preparation phase for the demanding eight-week RASP program. Candidates' entry and exit surveys were analyzed to better understand the relationship between candidates' reasons for volunteering and their goals in the organization. Candidates' Intrinsic Motivations and their Satisfaction upon arrival for pre-RASP best predicted candidate success. Specifically, candidates' desires to deploy or serve in combat, and the goal of earning credibility in the Rangers were the most important reasons and goals provided through candidates' open-text responses. Additionally, between-groups analyses between Black Candidates, Hispanic Candidates, and White Candidates showed that differences in candidate abilities and motivations better explains pre-RASP attrition than demographic proxies such as race or ethnicity.

A major healthcare problem is the overcrowding of hospitals and emergency departments which leads to negative patient outcomes and increased costs. In a previous study, performed by Leiden University Medical Centre, a new and innovative prehospital triage method was developed where two nurse paramedics could consult a cardiologist for patients with cardiac symptoms, via a live connection on a digital triage platform. The developed triage method resulted in a recall = 0.995 and specificity = 0.0113. This study arise the following research question: ‘Would there be enough (good) information gathered on the prehospital scene to make a machine learning model able to predict myocardial infarction?’. By testing different pre-processing steps, several features (premade ones and self-made ones), multiple models (Support Vector Machine, K Nearest Neighbour, Logistic Regression and Random Forest), various outcome settings and hyperparameters, led to the final results: recall = 0.995 and specificity = 0.1101. This is gained through the feature selected by a cardiologist and the Support Vector Machine model. The outcomes are controlled by an extra explainability layer named Explain Like I’m Five. This outcome illustrates that the created machine learning model is trained mostly on the right words and characters.