Sharing Linkable Learning Objects with the use of Metadata and a Taxonomy Assistant for Categorization

In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning courses content of the Drupal-based Content Management System G-Lorep for academic learning is exchanged designing an object incorporating metadata to support the reuse and the classification in its context. In such an Artificial Intelligence environment, the exchange of Linkable Learning Objects can be used for dialogue between Learning Systems to obtain information, especially with the use of semantic or structural similarity measures to enhance the existent Taxonomy Assistant for advanced automated classification

La Rete degli editori. Modelli di text-mining e network analysis a partire dai dati di aNobii

Obiettivo di questo contributo è quello di esaminare e discutere presupposti, metodi e risultati dell’analisi di dati estratti dalla piattaforma di social reading aNobii (http://www.anobii.com/) nell’ambito del progetto “Leggere in rete. Analisi delle pratiche di lettura in ambiente digitale”, in collaborazione tra Università degli Studi di Roma La Sapienza e Università degli Studi di Torino. Qui vengono presentati in particolare i risultati relativi all’analisi degli editori a partire non dai classici dati relativi sulla produzione editoriale rilevati annualmente da Istat ma a partire dalle recensioni dei libri inserite dai lettori sulla piattaforma aNobii. La ricerca è stata condotta secondo due prospettive tra loro integrate: una orientata a definire e visualizzare, in forma di grafo, la rete degli editori, e si avvale di strumenti ed euristiche situati nel campo della network science; l’altra, a partire dalla segmentazione degli editori realizzata attraverso le metriche di rete, analizza i vocabolari relativi a ciascun editore e ne individua le specificità, attraverso le tecniche dell’analisi automatica dei testi

Making meaning of grade 10 History textbook back covers

“Do not judge a book by its cover” is a phrase often heard. It reminds us not to limit the judgement of a book to just the front cover. The front cover is the first interaction with the book. It draws interest making one want to know more about the book. The front cover does not reveal the entire contents of the book. However, the back cover helps the buyer to make an informed decision in relation to the book’s relevance in topic and ideas as well as the major decision whether to buy the book or move on to the next. For this and other reasons, this study was conducted to make meaning of Grade 10 History textbook back covers. This qualitative study was informed by the interpretivist paradigm and aimed to investigate the multi-layered meanings that may arise when textbook back cover elements are analysed to discover their meaning and intention. A purposive sample of four Department of Basic Education (DBE) approved Grade 10 History textbooks were chosen to investigate the phenomenon – making meaning. Denotation and connotation as branches of philology and iconography served as key analysing methods and provided an all-encompassing meaning of the back covers. Through the application of critical discourse analysis (CDA), four moves, namely commercial, design and layout, academic and curriculum or nature of history and their steps were identified on the covers of the selected history textbooks. These four moves are investigated with special emphasis on academics and the nature of history as these should directly reference the Curriculum and Assessment Policy Statement History curriculum. My study showed how the visual and historical elements displayed on the back covers have political, social, cultural, commercial and educational elements. These elements direct the historical narratives of the books clarifying what should be present and what is missing while combining to make meaning of the back covers.Dissertation (MEd (History Education))--University of Pretoria, 2021.Humanities EducationMEd (History Education)Unrestricte

Automated classification of book blurbs according to the emotional tags of the social network Zazie

Sentiment Analysis and Opinion Mining are receiving increasing attention in many sectors because knowing and predicting opinions of people is considered a strategic added value. In the last years an increasing attention has also been devoted to Emotion Recognition, often by developing automated systems that can associate user's emotions to texts, music or artworks. Zazie is an Italian social network for readers that introduces a new dimension on book characterization, the emotional icon tagging. Each book, besides user's comments and reviews, can be tagged with special icons, the moods, that are emotional tags chosen by the users. The aim of this work is to study the feasibility of an automated classification of books in Zazie according to the emotional tags, by means of the lexical analysis of book blurbs. A supervised learning approach is used to determine if a correlation between the characteristics of a book blurb and the emotional icons associated to the book by the users exists

Emotional book classification from book blurbs

Knowing and predicting opinions of people is considered a strategic added value, interpreting the qualia i.e., the subjective nature of emotional content. The aim of this work is to study the feasibility of an emotion recognition and automated classification of books according to emotional tags, by means of a lexical and semantic analysis of book blurbs. A supervised learning approach is used to determine if a correlation exists between the characteristics of a book blurb and emotional icons associated to the book by users. In this paper the underlying idea of the system is presented, the preprocessing and features extraction phases are described and experimental results on the social network Zazie and its mood tags are discussed

Information models in sentiment analysis based on linguistic resources

Почетак новог миленијума обележен је бурним развојем друштвених мрежа, интернет технологијама у облаку и применом вештачке интелигенције у веб алатима. Изузетно брз раст броја текстова на интернету (блогова, сајтова за електронску трговину, форума, дискусионих група, система за пренос кратких порука, друштвених мрежа и портала за објаву вести) увећао је потребу за развојем метода брзе, свеобухватне и прецизне анализе текста. Због тога је значајан развој језичких технологија чији су примарни задаци: класификација докумената (енг. Document classification), груписање докумената (енг. Document clustering), проналажење информација (енг. Information Retrieval), разрешавање значења вишезначних речи (енг. Word-sense disambiguation), екстракција из текста (енг. Text еxtraction), машинско превођење (енг. Machine translation), рачунарско препознавање говора (енг. Computer speech recognition), генерисање природног језика (енг. Natural language generation), анализа осећања (енг. sentiment analysis), итд. У рачунарској лингвистици данас је у употреби више различитих назива за област чији је предмет интересовања обрада осећања у тексту: класификација према осећању (енг. sentiment classification), истраживање мишљење (енг. opinion mining), анализа осећања (енг. sentiment analysis), екстракција осећања (енг. sentiment extraction). По својој природи и методама које користи, анализа осећања у тексту спада у област рачунарске лингвистике која се бави класификацијом текста. У процесу обраде осећања се, у општем случају, говори о три врсте класификације текстова:...The beginning of the new millennium was marked by huge development of social networks, internet technologies in the cloud and applications of artificial intelligence tools on the web. Extremely rapid growth in the number of articles on the Internet (blogs, e-commerce websites, forums, discussion groups, and systems for transmission of short messages, social networks and portals for publishing news) has increased the need for developing methods of rapid, comprehensive and accurate analysis of the text. Therefore, remarkable development of language technologies has enabled their applying in processes of document classification, document clustering, information retrieval, word sense disambiguation, text extraction, machine translation, computer speech recognition, natural language generation, sentiment analysis, etc. In computational linguistics, several different names for the area concerning processing of emotions in text are in use: sentiment classification, opinion mining, sentiment analysis, sentiment extraction. According to the nature and the methods used, sentiment analysis in text belongs to the field of computational linguistics that deals with the classification of text. In the process of analysing of emotions we generally speak of three kinds of text classification:..