Twitter är en av de populäraste mikrobloggarna, som används för att uttryckatankar och åsikter om olika ämnen. Ett område som har dragit till sig mycketintresse under de senaste åren är twittersentimentanalys. Twittersentimentanalyshandlar om att bedöma vad för sentiment ett inlägg på Twitter uttrycker, om detuttrycker någonting positivt eller negativt. Olika metoder kan användas för attutföra twittersentimentanalys, där vissa lämpar sig bättre än andra. De vanligastemetoderna för twittersentimentanalys använder maskininlärning.Syftet med denna studie är att utvärdera tre stycken klassificeringsalgoritmerinom maskininlärning och hur märkningen av en datamängd påverkar en klassifi-ceringsmodells förmåga att märka ett twitterinlägg korrekt för twittersentimenta-nalys. Naive Bayes, Support Vector Machine och Convolutional Neural Network ärklassificeringsalgoritmerna som har utvärderats. För varje klassificeringsalgoritmhar två klassificeringsmodeller tagits fram, som har tränats och testats på två se-parata datamängder: Stanford Twitter Sentiment och SemEval. Det som skiljer detvå datamängderna åt, utöver innehållet i twitterinläggen, är märkningsmetodenoch mängden twitterinlägg. Utvärderingen har gjorts utefter vilken prestanda deframtagna klassificeringmodellerna uppnår på respektive datamängd, hur lång tidde tar att träna och hur invecklade de var att implementera.Resultaten av studien visar att samtliga modeller som tränades och testades påSemEval uppnådde en högre prestanda än de som tränades och testades på Stan-ford Twitter Sentiment. Klassificeringsmodellerna som var framtagna med Convo-lutional Neural Network uppnådde bäst resultat över båda datamängderna. Dockär ett Convolutional Neural Network mer invecklad att implementera och tränings-tiden är betydligt längre än Naive Bayes och Support Vector Machine. / Twitter is one of the most popular microblogs, which is used to express thoughtsand opinions on different topics. An area that has attracted much interest in recentyears is Twitter sentiment analysis. Twitter sentiment analysis is about assessingwhat sentiment a Twitter post expresses, whether it expresses something positiveor negative. Different methods can be used to perform Twitter sentiment analysis.The most common methods of Twitter sentiment analysis use machine learning.The purpose of this study is to evaluate three classification algorithms in ma-chine learning and how the labeling of a data set affects classification models abilityto classify a Twitter post correctly for Twitter sentiment analysis. Naive Bayes,Support Vector Machine and Convolutional Neural Network are the classificationalgorithms that have been evaluated. For each classification algorithm, two classi-fication models have been trained and tested on two separate data sets: StanfordTwitter Sentiment and SemEval. What separates the two data sets, in addition tothe content of the twitter posts, is the labeling method and the amount of twitterposts. The evaluation has been done according to the performance of the classifi-cation models on the respective data sets, training time and how complicated theywere to implement.The results show that all models trained and tested on SemEval achieved ahigher performance than those trained and tested on Stanford Twitter Sentiment.The Convolutional Neural Network models achieved the best results over both datasets. However, a Convolutional Neural Network is more complicated to implementand the training time is significantly longer than Naive Bayes and Support VectorMachine.

The new General Data Protection Regulation (GDPR) Act will apply to all companies within the European Union after 25 May. This means stricter legal requirements for companies that in some way store personal data. The goal of this project is therefore to make it easier for companies to meet the new legal requirements. This by creating a tool that searches file systems and visually shows the user in a graphical user interface which files contain personal data. The tool uses Named entity recognition with the Linear chain conditional random field algorithm which is a type of supervised learning method in machine learning. This algorithm is used in the project to find names and addresses in files. The different models are trained with different parameters and the training is done using the stanford NER library in Java. The models are tested by a test file containing 45,000 words where the models themselves can predict all classes to the words in the file. The models are then compared with each other using the measurements of precision, recall and F-score to find the best model. The tool also uses Regular Expression to find emails, IP numbers, and social security numbers. The result of the final machine learning model shows that it does not find all names and addresses, but that can be improved by increasing exercise data. However, this is something that requires a more powerful computer than the one used in this project. An analysis of how the Swedish language is built would also need to be done to apply the most appropriate parameters for the training of the model. / Den nya lagen General data protection regulation (GDPR) började gälla för alla företag inom Europeiska unionen efter den 25 maj. Detta innebär att det blir strängare lagkrav för företag som på något sätt lagrar personuppgifter. Målet med detta projekt är därför att underlätta för företag att uppfylla de nya lagkraven. Detta genom att skapa ett verktyg som söker igenom filsystem och visuellt visar användaren i ett grafiskt användargränssnitt vilka filer som innehåller personuppgifter. Verktyget använder Named Entity Recognition med algoritmen Linear Chain Conditional Random Field som är en typ av ”supervised” learning metod inom maskininlärning. Denna algoritm används för att hitta namn och adresser i filer. De olika modellerna tränas med olika parametrar och träningen sker med hjälp av biblioteket Stanford NER i Java. Modellerna testas genom en testfil som innehåller 45 000 ord där modellerna själva får förutspå alla klasser till orden i filen. Modellerna jämförs sedan med varandra med hjälp av mätvärdena precision, recall och F-score för att hitta den bästa modellen. Verktyget använder även Regular expression för att hitta e- mails, IP-nummer och personnummer. Resultatet på den slutgiltiga maskininlärnings modellen visar att den inte hittar alla namn och adresser men att det är något som kan förbättras genom att öka träningsdata. Detta är dock något som kräver en kraftfullare dator än den som användes i detta projekt. En undersökning på hur det svenska språket är uppbyggt skulle även också behöva göras för att använda de lämpligaste parametrarna vid träningen av modellen.

The problem presented in this study is to determine if any differences exist in the sociometric standing and the scholastic achievement between children from broken homes and children from unbroken homes. The purpose of the study is to make a comparison of the sociometric standing and scholastic achievement of two selected groups of pupils to determine if differences exist and to what extent they exist.

Not many studies exist in the literature on reading in South Africa which examine the differences between the reading performance of first (L1) and second (L2) language English speaking learners, particularly those who experience barriers to learning. Using archival material from the Education Clinic of the University of the Witwatersrand, this study compared the results on the Stanford Diagnostic Reading Test (Brown Level) for a group of 43 high school L1 (20) and L2 (23) learners identified as experiencing barriers to learning. In line with international research on reading difficulties skills (Ben-Zeev, 1984; Baker, 1988; Drucker, 2003; Cummins, 1989,1991; Miller, 1984; Droop and Verhoeven, 1998), it was found that the L2 students performed significantly below the level of their L1 counterparts in Auditory Vocabulary and Reading Comprehension. The results on the Phonetic Analysis were found to be similar for both groups.

Thesis (M.S.)--University of Tennessee, Knoxville, 2003. / Title from title page screen (viewed Mar. 22, 2004). Thesis advisor: Carol P. Harden. Document formatted into pages (x, 149 p. : col. ill., col. maps). Vita. Includes bibliographical references (p. 72-78).

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Learner’s processing styles may play a vital role in their approach to learning, more specifically; the ability to make inferences plays an important role in all areas of language and learning and may contribute to difficulties learners are experiencing at school. It is therefore that the research was directed at investigating a possible relationship between the left hemispheric analytical and right hemispheric holistic processing styles and the types of errors inferential versus literal, made in reading comprehension tasks. The hemispheric processing styles were operationalised as the approach taken to the Rey- Osterreith Complex Figure (ROCF) and the types of errors made on the Stanford Diagnostic Reading Test (SDRT) across two levels of educational development. The sample consisted of grade 4 and grade 10 model C learners from the same schooling district. The data obtained from both assessments were subjected to correlation analyses, chi squared tests, analyses of variances (ANOVAs) and logistic regressions. Finally the results and associative conclusions indicated that there were only modest positive relationships between the predominant hemispheric processing styles and the error types on reading comprehension tasks and the demographics of the learners were the main contributors and accounted for the results discovered in the study as opposed to general hemispheric processing. Thus there is a need to understand the unique dynamics within the country and to explore alternatives to teaching practices to account for the variations evident in the classrooms.

The purpose of this study is: 1) To point out some similarities and differences of the two school systems in the twin cities. 2)To determine the comparative readiness of the students finalizing the seventh grade in Texas and those finishing eighth grade in Arkansas to pursue high school work. 3) To make a comparative study of the progress made during the Freshman year in high school by these same students. 4) To determine the difference in achievement of students in the two school systems by making a study of the graduating seniors of the session 1936-1937.

Writing in 2001, musicologist Axel Klein concluded that Stanford’s reception history has been significantly impacted by the complicated national identities surrounding both the composer and his music. A lifelong devotee of the nineteenth-century Austro-Germanic tradition, Stanford’s status as an Irish-born leading figure of the ‘English’ Musical Renaissance has compromised the place that the composer and his musical output occupy within the history of Western music. Stanford is well-known for being an outspoken critic on matters musical and Irish. Although his views seldom appear ambiguous, there is still a sense that the real Stanford remains partially obscured by his opinions. Through an examination of his symphonic works, this thesis seeks to readdress our understanding of Stanford and his relationship with Ireland and the musical community of his time. Although A. Peter Brown has stated that the symphony was not a central genre for the composer, it is my argument that, on the contrary, the symphony was a pivotal form for him. Considering these works within the broader history of the symphony in Europe in the nineteenth century, and through a critical examination of Stanford’s relationship with Ireland, this thesis seeks to demonstrate that these seven works can be read as an allegory for the composer’s relationship both with his homeland and with the musical community of his time. His struggle to combine the universality of symphonic expression with a need to articulate his Irish identity parallels Stanford’s own attempts to integrate himself within both British and European musical communities, and further demonstrates, in his eventual rejection of it, that it was only when he attempted to forge a more individualistic path through his music that he found a way of expressing his individual Irish identity.