Syftet med följande uppsats är att genomföra en intersektionell analys av några av Bruce Springsteens låttexter skrivna under perioden 1978-1984. Avsikten är att undersöka hur maktstrukturer uppkommer beroende av kön, sexualitet, klass och etnicitet. Vidare är målet att diskutera hur man kan arbeta med Springsteens texter i gymnasieskolans svenskämne. Materialet som används är de låttexter under nämnda period som bäst belyser hur maktstrukturer uppkommer. I texterna finns makt och inflytande i samhället till största del hos männen. Eftersom Springsteen av media betecknas som en företrädare för den vanliga människan är deltagandet i arbetslivet trots det inte till för alla. Maktstrukturer beror också på att människor berövas möjligheten att tala samt familjestrukturer.

The purpose of this study has been to examine how Buddhism has been presented and represented in textbooks for Religious Education in Upper secondary school between 2003 and 2013. Four textbooks have been used for the study, where two are linked to the new curriculum from 2011 and two are the older versions. By using comparative, narrative and visual text analyses the study examines how historical context, figures and Buddhism of today are represented in the textbooks. The study shows a shifting change in the textbooks. The study concludes that the textbooks that are linked to the new curriculum from 2011 have highlighted the role Buddha as a down-to-earth person, which is not the case in the older textbooks. Simultaneously, the new textbooks have chosen to focus more on teachings of Buddhism and to exclude the historical context to a certain degree, something that was dominant among the older textbooks.  Image analysis also shows a change between older and newer textbooks, where most of the pictures in older textbooks show a dim and closed environment for the viewer's eyes. The changes in the newer textbooks provide greater diversity in the presentation of Buddhism, where today's audience may enjoy not only an illustration of Buddhism but many varieties of Buddhism through the narrative and pictures.

This dissertation addresses the problem of analysing digital data in digital forensics. It will be shown that text mining methods can be adapted and applied to digital forensics to aid analysts to more quickly, efficiently and accurately analyse data to reveal truly useful information. Investigators who wish to utilise digital evidence must examine and organise the data to piece together events and facts of a crime. The difficulty with finding relevant information quickly using the current tools and methods is that these tools rely very heavily on background knowledge for query terms and do not fully utilise the content of the data. A novel framework in which to perform evidence discovery is proposed in order to reduce the quantity of data to be analysed, aid the analysts' exploration of the data and enhance the intelligibility of the presentation of the data. The framework combines information extraction techniques with visual exploration techniques to provide a novel approach to performing evidence discovery, in the form of an evidence discovery system. By utilising unrestricted, unsupervised information extraction techniques, the investigator does not require input queries or keywords for searching, thus enabling the investigator to analyse portions of the data that may not have been identified by keyword searches. The evidence discovery system produces text graphs of the most important concepts and associations extracted from the full text to establish ties between the concepts and provide an overview and general representation of the text. Through an interactive visual interface the investigator can explore the data to identify suspects, events and the relations between suspects. Two models are proposed for performing the relation extraction process of the evidence discovery framework. The first model takes a statistical approach to discovering relations based on co-occurrences of complex concepts. The second model utilises a linguistic approach using named entity extraction and information extraction patterns. A preliminary study was performed to assess the usefulness of a text mining approach to digital forensics as against the traditional information retrieval approach. It was concluded that the novel approach to text analysis for evidence discovery presented in this dissertation is a viable and promising approach. The preliminary experiment showed that the results obtained from the evidence discovery system, using either of the relation extraction models, are sensible and useful. The approach advocated in this dissertation can therefore be successfully applied to the analysis of textual data for digital forensics Copyright / Dissertation (MSc)--University of Pretoria, 2010. / Computer Science / unrestricted

Science education research continues to demonstrate improved learning with active-learning techniques compared to lectures. However, the question of which active-learning methods are the most effective for learning complex scientific principles in various context still remains. Models are commonly used in activities that allow students to simplify complex systems and understand how components interact. I investigated the outcomes for student learning and engagement of two model-based activities - concept models and game simulations. The activities were conducted in an introductory biology course in sixteen discussion sections. Eight sections were assigned to the concept model activity and eight to the simulation activity. To assess engagement, students filled out a Likert-scale questionnaire on enjoyment and usefulness of activity (concept model: 130 students for food web activity and 131 for carbon cycle activity; game simulation: 131 students for food web activity and 126 game simulation students during the carbon cycle activity). To assess student learning, 152 students completed pre-post homework assignment based on conservation and transformation of matter. Over 80% of students enjoyed both the concept-mapping and simulation activities. Students reported that the hands-on nature of the concept activity was helpful for understanding the connections in food webs. For the homework assessment, all students significantly increased in their scores from pre to post on the MC (paired t-test, meanpre = 4.86±1.6; meanpost = 5.23±1.6;p<.05) and TF assessments (paired t-test; meanpre = 2.06±1.0 meanpost = 2.32± 1.0; p<0.05). For the TF assessments, we observed the trend that students in the simulation group showed a greater improvement in their scores than students in the concept-mapping group (t-test; meanΔconcept = 0.11±1.4; meanΔsimulation =0 .43±1.0 p=.059). There was no difference between student improvement for the two groups on the MC assessment ( t-test meanΔconcept = 0.27±2.1; meanΔsimulation = 0.51±1.8 p=.474). Students’ responses to short answer questions showed those students’ ideas about the concept of matter conservation varied from naive to scientific. For example, students failed to conserve matter during nutrient cycling. More scientific responses demonstrated principled reasoning such as references to conservation of matter. The students within the two activities did not demonstrate large differences between their text responses for the short answer. Overall, students in both activity type demonstrated learning gains, though there was no significant difference between the activity types.

Emotions are among the most pervasive aspects of human experience. They have long been of interest to social and behavioural sciences. Recently, emotions have attracted the attention of researchers in computer science and particularly in computational linguistics. Computational approaches to emotion analysis have also focused on various emotion modalities, but there is less effort in the direction of automatic recognition of the emotion expressed. Although some past work has addressed detecting emotions, detecting why an emotion arises is ignored. In this work, we explore the task of classifying texts automatically by the emotions expressed, as well as detecting the reason why a particular emotion is felt. We believe there is still a large gap between the theoretical research on emotions in psychology and emotion studies in computational linguistics. In our research, we try to fill this gap by considering both theoretical and computational aspects of emotions. Starting with a general explanation of emotion and emotion causes from the psychological and cognitive perspective, we clarify the definition that we base our work on. We explain what is feasible in the scope of text and what is practically doable based on the current NLP techniques and tools. This work is organized in two parts: first part on Emotion Expression and the second part on Emotion Stimulus. In emotion expression detection, we start with shallow methodologies, such as corpus-based and lexical-based, followed by deeper methods considering syntactic and semantic relations in text. First, we demonstrate the usefulness of external knowledge resources, such as polarity and emotion lexicons, in automatic emotion detection. Next, we provide a description of the more advanced features chosen for characterizing emotional content based on the syntactic structure of sentences, as well as the machine learning techniques adopted for emotion classification. The main novelty of our learning methodology is that it breaks down a problem into hierarchical steps. It starts from a simpler problem to solve, and uses what is learnt to extend the solution to solve harder problems. Here, we are learning emotion of sentences with one emotion word and we are extending the solution to sentences with more than one emotion word. Next, we frame the detection of causes of emotions as finding a stimulus frame element as defined for the emotion frame in FrameNet – a lexical database of English based on the theory of meaning called Frame Semantics, which was built by manually annotating examples of how words are used in actual texts. According to FrameNet, an emotion stimulus is the person, event, or state of affairs that evokes the emotional response in the Experiencer. We believe it is the closest definition to emotion cause in order to answer why the experiencer feels that emotion. We create the first ever dataset annotated with both emotion stimulus and emotion class; it can be used for evaluation or training purposes. We applied sequential learning methods to the dataset. We explored syntactic and semantic features in addition to corpus-based features. We built a model which outperforms all our carefully-built baselines. To show the robustness of our model and to study the problem more thoroughly, we apply those models to another dataset (that we used for the first part as well) to go deeper than detecting the emotion expressed and also detect the stimulus span which explains why the emotion was felt. Although we first address emotion expression and emotion stimulus independently, we believe that an emotion stimulus and the emotion itself are not mutually independent. In the last part, we address the relation of emotion expression and emotion stimulus by building four cases: both emotion expression and emotion stimulus occur at the same time, none of them appear in the text, there is only emotion expression, or only the emotion stimulus exists while there is no explicit mention of the emotion expressed. We found the last case the most challenging, so we study it in more detail. Finally, we showcase how a clinical psychology application can benefit from our research. We also conclude our work and explain the future directions of this research. Note: see http://www.eecs.uottawa.ca/~diana/resources/emotion_stimulus_data/ for all the data built for this thesis and discussed in it.

This diploma thesis presents Big Data technologies and their possible use cases and applications. Theoretical part is initially focused on definition of term Big Data and afterwards is focused on Big Data technology, particularly on Hadoop framework. There are described principles of Hadoop, such as distributed storage and data processing, and its individual components. Furthermore are presented the largest vendors of Big Data technologies. At the end of this part of the thesis are described possible use cases of Big Data technologies and also some case studies. The practical part describes implementation of demo example of Big Data technologies and it is divided into two chapters. The first chapter of the practical part deals with conceptual design of demo example, used products and architecture of the solution. Afterwards, implementation of the demo example is described in the second chapter, from preparation of demo environment to creation of applications. Goals of this thesis are description and characteristics of Big Data, presentation of the largest vendors and their Big Data products, description of possible use cases of Big Data technologies and especially implementation of demo example in Big Data tools from IBM.

The aim of this study was to examine differences and similarities in the news coverage among the presidential candidates Joe Biden and Donald Trump in the presidential election 2020. We also examined how the candidates are framed in the different newspapers. In this study, similarities and differences regarding national variances in news articles from Swedish and American newspapers, also where a important part of the research. We did a qualitative text analysis based on 48 online articles from 6 different newspapers, the 3 largest from each country. The newspapers in question are USA Today, The Wall Street Journal and The New York Times from USA and Aftonbladet, Dagens Nyheter and Expressen from Sweden. The result showed that there were more differences between the two candidates than similarities, in the way they were presented in news media. Donald Trump was often portrayed as the president that had a very harsh tone in his statements, and he was throwing out accusations towards Biden and the Democrats without having any facts to back them up. This made him look rather ignorant and sometimes even dumb. Biden, on the other hand, was portrayed as the one who is more fitting for the role as president, based on the writings about his previous experience, and many of the articles in the analysis portrayed him as a friendly family man. The focus point on both candidates during the election was who was leading the election, who was leading in which state, and how much that specific candidate was winning by. This was a topic that frequently showed up in articles from both countries and they often portrayed the whole election as some kind of contest or game, instead of focusing on what the candidates wanted to do and change about the country. The American newspapers more frequently wrote about the statistics when it came to who was leading the election, and their news coverage wasn't as clear as the Swedish news coverage. The Swedish newspaper often used secondary sources, for example from American newspapers, since they weren't able to be where the election takes place, which means they are re-telling the things that happened during the election. Other than that, there weren't that many differences between the news coverage in the US and Sweden.

Several studies have been made on the crisis communication efforts made by politicians and athletes following a personal crisis, but few focus on the efforts made by other people of influence whose image and personal brand get hurt during a crisis. The aim of this study is to examine nine of Swedish journalist Fredrik Virtanen’s statements after he was accused of rape and sexual assault by several women during #metoo. The theoretical framework is based on Goffman’s theories on impression management and Benoit’s image repair theory. The chosen method for this study is a combined qualitative text analysis and critical rhetorical analysis. The results show that a variety of image repair strategies are visible in Virtanen's communication and that the most frequently used strategies aresimple denial, differentiation, attack accuser and minimization. The results support earlier studies on image repair that claim that the cultural and societal as well as the circumstantial context is an important aspect in the success or failure of an image repair effort. The results also imply that without support from people or organisations that have previously been affiliated with the accused, who can assist in the communication efforts, the accused will have a harder time repairing their image.

Uppsatsen syftar till att studera hur gymnasieskolor marknadsför sig själva genomspråkbruket på deras hemsidor. Detta tillämpas genom en text- och diskursanalytisk studie avtre gymnasieskolor som tillgodoser Samhällsvetenskapsprogrammet i Täby kommun. Urvalethar begränsats till respektive skolas hemsida och programsida. Resultatet visar på att det finnsskillnader i hur skolorna använder språket för att marknadsföra skolan och detsamhällsvetenskapliga programmet. Åva gymnasium använder sig av direkta frågor tillläsaren, Täby Enskilda har ett direkt tilltal till läsaren i formen du och Tibble gymnasiumlyfter fram lärarnas kunnighet. / This essay aims to study how secondary schools’ market themselves through the use oflanguage on their websites. This is applied through a text and discourse analytical study ofthree secondary schools, all of which meet the Social Sciences program, located in Täby. Theselection has been limited to each school's website and program page. The results show thatthere are differences in how the schools use the language to market the school and the socialscience program. Åva gymnasium uses direct questions to the reader, Täby Enskilda have adirect address to the reader in the form you and Tibble gymnasium highlight the teachers'knowledge.

Hierdie studie handel oor die Engelse onderskrifte in Afrikaanse televisieprogramme oor verskillende genres. Dit ondersoek die verskynsels asook die gehalte in en van die intertalige onderskrifte. Dit is _ vergelykende teksstudie met die doel om aanbevelings vir verbeterde onderskrifte te gee. Daar word _ deeglike literatuurstudie gedoen om agtergrond vir die navorsing te skep. Die data word ingesamel en gekategoriseer om geanaliseer te word sodat ingeligte gevolgtrekkings gemaak kan word. Daar is bevind dat die Afrikaanse televisieprogramme wat ondersoek is, substandaard Engelse onderskrifte het en dat die meeste foute is as gevolg van die minagting van die onderskrifskeppingsreëls. Die verhandeling sluit af deur aanbevelings te maak oor hoe dit verbeter kan word. / This study considers English subtitles in Afrikaans television programs in different genres. It researches the phenomena, as well as the standard of the interlingual subtitles. It is a comparative text study and has the goal of creating recommendations for better subtitles. A thorough literature study was done to provide background for the coming research. The data was collected and categorised to be analysed to reach an informed conclusion. The study found that the researched Afrikaans television programs have substandard subtitles and that the most mistakes were because of ignoring the subtitling rules. The dissertation concludes by making suggestions on how to better it. / Dissertation (MA)--University of Pretoria, 2019. / Afrikaans / MA / Unrestricted