Most of the Holocaust's victims were never able to tell their stories, and of the millions of victims, only a few hundred were able to write about their experiences. This makes surviving personal testimonies precious in many ways. They provide a rich resource for understanding both individual experience, as well as the ways in which the socio-historical context (i.e. region, gender, and class) greatly influenced each distinctive experience. This study examines six Holocaust diaries, of Jewish victims, taken from three different parts of occupied Europe: from Poland, Janusz Korczak's Ghetto Diary and Chaim Kaplan's The Scroll of Agony; from Holland, Etty Hillesum's An Interupted Life:the Diaries, 1941-1943 and Letters from Westerbork and Anne Frank's Diary of a Young Girl; and lastly, from France, Helene Berr's Journal of Helene Berr and Raymond Raoul Lambert's Diary of a Witness, 1940-1943. Through an examination of these six diaries, this project analyzes how the personal experience of individuals who witnessed the period and chronicled its events helps us understand both the nature of the Holocaust experience and the specific local political, social, and economic contexts. This project argues that an examination of these texts, when studied alongside the histories of their specific local contexts, can reveal both what all victims shared, throughout Europe during the period, as well as what was localized- how the different horrors experienced, by the victims, created different versions of the same hell.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-04-19T13:35:36Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DISSERTAÇÃO_Franscisca Pamela Carvalho.pdf: 2272114 bytes, checksum: 83ff79a7d05409b93fe71ce4c307dc30 (MD5) / Made available in DSpace on 2017-04-19T13:35:36Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DISSERTAÇÃO_Franscisca Pamela Carvalho.pdf: 2272114 bytes, checksum: 83ff79a7d05409b93fe71ce4c307dc30 (MD5) Previous issue date: 2016-08-23 / Nos últimos anos, através da Internet, a comunicação se tornou mais ampla e acessível. Com o grande crescimento das redes sociais, blogs, sites em geral, foi possível estabelecer uma extensa base de conteúdo diversificado, onde os usuários apresentam suas opiniões e relatos pessoais. Esses informes podem ser relevantes para observações futuras ou até mesmo para o auxílio na tomada de decisão de outras pessoas. No entanto, essa massa de informação está esparsa na Web, em formato livre, dificultando a análise manual dos textos para categorização dos mesmos. Tornar esse trabalho automático é a melhor opção, porém a compreensão desses textos em formato livre não é um trabalho simples para o computador, devido a irregularidades e imprecisões da língua natural. Nessas circunstâncias, estão surgindo sistemas que classificam textos, de forma automática, por tema, gênero, características, entre outros, através dos conceitos da área de Mineração de Texto (MT). A MT objetiva extrair informações importantes de um texto, através da análise de um conjunto de documentos textuais. Diversos trabalhos de MT foram sugeridos em âmbitos variados como, por exemplo, no campo da psiquiatria. Vários dos trabalhos propostos, nessa área, buscam identificar características textuais para percepção de distúrbios psicológicos, para análise dos sentimentos de pacientes, para detecção de problemas de segurança de registros médicos ou até mesmo para exploração da literatura biomédica. O trabalho aqui proposto, busca analisar depoimentos pessoais de potenciais pacientes para categorização dos textos por tipo de transtorno mental, seguindo a taxonomia DSM-5. O procedimento oferecido classifica os relatos pessoais coletados, em quatro tipos de transtorno (Anorexia, TOC, Autismo e Esquizofrenia). Utilizamos técnicas de MT para o pré-processamento e classificação de texto, com o auxilio dos pacotes de software do Weka. Resultados experimentais mostraram que o método proposto apresenta alto índice de precisão e que a fase de pré-processamento do texto tem impacto nesses resultados. A técnica de classificação Support Vector Machine (SVM) apresentou melhor desempenho, para os fins apresentados, em comparação a outras técnicas usadas na literatura. / In the last few years, through the internet, communication became broader and more accessible. With the growth of social media, blogs, and websites in general, it became possible to establish a broader, diverse content base, where users present their opinions and personal stories. These data can be relevant to future observations or even to help other people’s decision process. However, this mass information is dispersing on the web, in free format, hindering the manual analysis for text categorization. Automating is the best option. However, comprehension of these texts in free format is not a simple task for the computer, taking into account irregularities and imprecisions of natural language. Giving these circumstances, automated text classification systems, by theme, gender, features, among others, are arising, through Text Mining (MT) concepts. MT aims to extract information from a text, by analyzing a set of text documents. Several MT papers were suggested on various fields, as an example, psychiatric fields. A number of proposed papers, in this area, try to identify textual features to perceive psychological disorders, to analyze patient’s sentiments, to detect security problems in medical records or even biomedical literature exploration. The paper here proposed aim to analyze potential patient’s personal testimonies for text categorization by mental disorder type, according to DSM-5 taxonomy. The offered procedure classifies the collected personal testimonies in four disorder types (anorexia, OCD, autism, and schizophrenia). MT techniques were used for pre-processing and text classification, with the support of software packages of Weka. Experimental results showed that the proposed method presents high precision values and the text pre-processing phase has impact in these results. The Support Vector Machine (SVM) classification technique presented better performance, for the presented ends, in comparison to other techniques used in literature.