The act of reading has benefits for individuals and societies, yet studies show that reading declines, especially among the young. Recommender systems (RSs) can help stop such decline. There is a lot of research regarding literary books using natural language processing (NLP) methods, but the analysis of textual book content to improve recommendations is relatively rare. We propose content-based recommender systems that extract elements learned from book texts to predict readers’ future interests. One factor that influences reading preferences is writing style; we propose a system that recommends books after learning their authors’ writing style. To our knowledge, this is the first work that transfers the information learned by an author-identification model to a book RS. Another approach that we propose uses over a hundred lexical, syntactic, stylometric, and fiction-based features that might play a role in generating high-quality book recommendations. Previous book RSs include very few stylometric features; hence, our study is the first to include and analyze a wide variety of textual elements for book recommendations. We evaluated both approaches according to a top-k recommendation scenario. They give better accuracy when compared with state-of-the-art content and collaborative filtering methods. We highlight the significant factors that contributed to the accuracy of the recommendations using a forest of randomized regression trees. We also conducted a qualitative analysis by checking if similar books/authors were annotated similarly by experts. Our content-based systems suffer from the new user problem, well-known in the field of RSs, that hinders their ability to make accurate recommendations. Therefore, we propose a Topic Model-Based book recommendation component (TMB) that addresses the issue by using the topics learned from a user’s shared text on social media, to recognize their interests and map them to related books. To our knowledge, there is no literature regarding book RSs that exploits public social networks other than book-cataloging websites. Using topic modeling techniques, extracting user interests can be automatic and dynamic, without the need to search for predefined concepts. Though TMB is designed to complement other systems, we evaluated it against a traditional book CB. We assessed the top k recommendations made by TMB and CB and found that both retrieved a comparable number of books, even though CB relied on users’ rating history, while TMB only required their social profiles.

The perception of a good user experience can be fundamental in an app for users to be willing to engage in it and use it more than one time. An important part of the user experience is the interactions of the used interface. In this study, the goal has been to answer the question 'How can swipe gestures be used in a book recommendation system to introduce users to new content and allow them to sample it'. To answer the question, a user study with 18 participants has been done in which prototypes based on three different swipe gestures, defined by Google, have been tested: edge swipe, overscroll collapse and paging swipe. The study is based on the previous studies 'Clicking, Assessing, Immersing, and Sharing' by Oh et al. and 'Power of the Swipe: Why Mobile Websites Should Add Horizontal Swiping to Tapping, Clicking, and Scrolling Interaction Techniques' by Dou and Sundar. The study shows that it is possible to design swipe gestures in a recommendation tool for books in such a fashion that it engages the users. The study also shows that the participants thought that the prototype with the paging swipe was statistically significantly more easy to browse and that their attention was less diverted while browsing the content of that prototype. How swipe gestures can be used in a book recommendation system for the intended purpose depends on the purpose and the context of the tool. One way to do it would be to create a tool based on the paging swipe gesture with factors such as content, attractiveness and intuitiveness of the interface in mind. / Upplevelsen av god användarvänlighet kan vara fundamental i en app för att användare ska vilja engagera sig i den och använda den fler än en gång. En viktig del av användarupplevelsen är interaktionerna i det använda gränssnittet. Målet i den här studien har varit att besvara frågan 'Hur kan swipegester användas i ett bokrekommendationsverktyg för att introducera användare till nytt material och låta dem sampla det'. För att besvara frågan har en användarstudie med 18 deltagare genomförts i vilken prototyper baserade på tre, av Google definierade, swipegester har testats: edge swipe, overscroll collapse och paging swipe. Studien är primärt baserad på de tidigare studierna 'Clicking, Assessing, Immersing, and Sharing' av Oh et al. och 'Power of the Swipe: Why Mobile Websites Should Add Horizontal Swiping to Tapping, Clicking, and Scrolling Interaction Techniques' av Dou och Sundar. Studien visar att det är möjligt att designa swipegester i ett bokrekommendationsverktyg på ett sådant sätt att det engagerar användare. Studien visar också att deltagarna fann prototypen med paging swipe statistiskt signifikant enklare att utforska och att deras uppmärksamhet var mindre delad när de utforskade innehållet i den prototypen. Studien skulle dock behöva genomföras med fler deltagare. Hur swipegester kan användas i ett bokrekommendationsverktyg beror på verktygets syfte liksom på kontexten det ska användas i. Ett sätt som swipegester skulle kunna användas på är i ett verktyg baserat på paging swipegester i vilket hänsyn tagits till faktorer som innehåll, attraktionskraft och gränssnittets intuitivitet.

Stylometry is the field of research aimed at defining features for quantifying writing style, and the most studied question in stylometry has been authorship attribution, where given a set of texts with known authorship, we are asked to determine the author of a new unseen document. In this study a number of lexical and syntactic stylometric feature sets were extracted for two datasets, a smaller one containing 27 books from 25 authors, and a larger one containing 11,063 books from 316 authors. Neural networks were used to transform the features into embeddings after which the nearest neighbor method was used to attribute texts to their closest neighbor. The smaller dataset achieved an accuracy of 91.25% using frequencies of 50 most common functional words, dependency relations, and Part-of-speech (POS) tags as features, and the larger dataset achieved 69.18% accuracy using a similar feature set with 100 most common functional words. In addition to performing author attribution, a user test showed the potentials of the model in generating author similarities and hence being useful in an applied setting for recommending books to readers based on author style. / Stilometri eller stilistisk statistik är ett forskningsområde som arbetar med att definiera särdrag för att kvantitativt studera stilistisk variation hos författare. Stilometri har mest fokuserat på författarbestämning, där uppgiften är att avgöra vem som skrivit en viss text där författaren är okänd, givet tidigare texter med kända författare. I denna stude valdes ett antal lexikala och syntaktiska stilistiska särdrag vilka användes för att bestämma författare. Experimentella resultat redovisas för två samlingar litterära verk: en mindre med 27 böcker skrivna av 25 författare och en större med 11 063 böcker skrivna av 316 författare. Neurala nätverk användes för att koda de valda särdragen som vektorer varefter de närmaste grannarna för de okända texterna i vektorrummet användes för att bestämma författarna. För den mindre samlingen uppnåddes en träffsäkerhet på 91,25% genom att använda de 50 vanligaste funktionsorden, syntaktiska dependensrelationer och ordklassinformation. För den större samlingen uppnåddes en träffsäkerhet på 69,18% med liknande särdrag. Ett användartest visar att modellen utöver att bestämma författare har potential att representera likhet mellan författares stil. Detta skulle kunna tillämpas för att rekommendera böcker till läsare baserat på stil.

Dans cette thèse, nous abordons le problème de l'analyse des sentiments. Plus précisément, nous sommes intéressés à analyser le sentiment exprimé dans les textes de médias sociaux.Nous allons nous concentrer sur deux tâches principales: la détection de polarité de sentiment dans laquelle nous cherchons à déterminer la polarité (positive, négative ou neutre) d'un texte donné et l'extraction de cibles d’opinion et le sentiment exprimé vers ces cibles (par exemple, pour le restaurant nous allons extraire des cibles comme la nourriture, pizza, service). Notre principal objectif est de construire des systèmes à la pointe de la technologie qui pourrait faire les deux tâches. Par conséquent, nous avons proposé des systèmes supervisés différents suivants trois axes de recherche: l'amélioration de la performance du système par la pondération de termes, en enrichissant de la représentation de documents et en proposant un nouveau modèle pour la classification de sentiment.Pour l'évaluation, nous avons participé à un atelier international sur l'évaluation sémantique (Sem Eval), nous avons choisi deux tâches: l'analyse du sentiment sur Twitter dans laquelle nous déterminer la polarité d'un tweet et l'analyse des sentiments basée sur l’aspect dans laquelle nous extrayons les cibles d'opinion dans les critiques de restaurants, puis nous déterminons la polarité de chaque cible, nos systèmes ont été classés parmi les premiers trois meilleurs systèmes dans toutes les sous-tâches. Nous avons également appliqué nos systèmes sur un corpus des critiques de livres français construit par l'équipe Open Edition pour extraire les cibles d'opinion et leurs polarités. / In this thesis, we address the problem of sentiment analysis. More specifically, we are interested in analyzing the sentiment expressed in social media texts such as tweets or customer reviews about restaurant, laptop, hotel or the scholarly book reviews written by experts. We focus on two main tasks: sentiment polarity detection in which we aim to determine the polarity (positive, negative or neutral) of a given text and the opinion target extraction in which we aim to extract the targets that the people tend to express their opinions towards them (e.g. for restaurant we may extract targets as food, pizza, service).Our main objective is constructing state-of-the-art systems which could do the two tasks. Therefore, we have proposed different supervised systems following three research directions: improving the system performance by term weighting, by enriching the document representation and by proposing a new model for sentiment classification. For evaluation purpose, we have participated at an International Workshop on Semantic Evaluation (SemEval), we have chosen two tasks: Sentiment analysis in twitter in which we determine the polarity of a tweet and Aspect-Based sentiment analysis in which we extract the opinion targets in restaurant reviews, then we determine the polarity of each target. Our systems have been among the first three best systems in all subtasks. We also applied our systems on a French book reviews corpus constructed by OpenEdition team for extracting the opinion targets and their polarities.