An Adaptive Contextual Recommender System: a Slow Intelligence Perspective

This paper introduces an Adaptive Context Aware Recommender system based on the Slow Intelligence approach. The system is made available to the user as an adaptive mobile application, which allows a high degree of customization in recommending services and resources according to his/her current position and global profile. A case study applied to the town of Pittsburgh has been analyzed considering various users (with different profiles as visitors, students, professors) and an experimental campaign has been conducted obtaining interesting result

Sentiment Analysis Application with Social Media Network Integration

Η εξαγωγή και ανάλυση συναισθήματος αποτελεί τομέα εφαρμογών της Επεξεργασίας Φυσικής Γλώσσας, στοχεύοντας στην εξαγωγή γνώμης και συναισθημάτων από εισερχόμενα κείμενα. Η παρούσα μελέτη και Διπλωματική Εργασία αυτή έχει ως σκοπό την δημιουργία μιας διαδικτυακής πλατφόρμας για εξαγωγή συναισθήματος από γραπτά κείμενα των μεσών κοινωνικής δικτύωσης. Συγκεκριμένα, αποτελείται από μοντέλο μηχανικής μάθησης το οποίο επιτρέπει την ανάλυση συναισθήματος από κείμενα και την κατηγοριοποίησή τους σε θετικά, αρνητικά ή ουδέτερα, καθώς και μιας διαδικτυακής εφαρμογής η οποία παρέχει σε χρήστες τη δυνατότητα να αλληλεπιδρούν με το μοντέλο αυτό και να εξάγουν συναισθηματική πληροφορία από τα κείμενα που εισάγουν. Επιπροσθέτως, δίνεται η δυνατότητα αναζήτησης λέξεων-κλειδιών σε δύο διαφορετικά είδη κειμένων (tweets και κριτικές) και σε διαφορετικά μέσα κοινωνικής δικτύωσης όπως είναι το Twitter και το Reddit, για την ανάλυση σχολίων, καθώς επίσης και η προβολή διαγραμμάτων με στοιχεία σχετικά με τους χρήστες που διατύπωσαν τα σχόλια. Δίνεται η δυνατότητα εξαγωγής σχολίων και ανάλυσης του συναισθηματικού περιεχομένου τους και από βίντεο της πλατφόρμας Youtube. Για την υλοποίηση των στοιχείων της εργασίας χρησιμοποιήθηκαν οι γλώσσες προγραμματισμού Python και JavaScript. Η παρούσα μελέτη περιλαμβάνει εκτενή περιγραφή του μοντέλου προβλέψεων και του τρόπου δημιουργίας του. Περιγράφεται επίσης αναλυτικά η υλοποίηση καθώς και η προ-επεξεργασία που υπόκειται το κείμενο πριν δοθεί στο μοντέλο μηχανικής μάθησης για την εξαγωγή συναισθήματος. Τέλος, προτείνονται βελτιώσεις και επεκτάσεις που θα μπορούσαν να γίνουν στην εν λόγω πλατφόρμα μελλοντικά, προσφέροντας ακόμη περισσότερες δυνατότητες και λειτουργίες στους χρήστες της εφαρμογής.Sentiment Analysis is an application domain of Natural Language Processing focusing in extracting sentiment and opinion from textual input. The purpose of the present Thesis is the creation of a web platform for extracting sentiment from texts. Specifically, the designed and implemented platform consists of a machine learning model that manages to retrieve sentiment from a text by categorizing the input into three different classes: positive, negative or neutral. Additionally, this model can be used via a web application processing two different types of text input, namely tweets and (movie) reviews. The platform also provides the possibility to retrieve comments and opinions from different social media platforms such as Reddit, Twitter and Youtube by searching any keyword and classify the results. The results are presented with a distinctive visualization to the users, giving a better perspective of what people think about specific topics. For the development of the components of the present project and application, the Python and JavaScript programming languages have been utilized. The machine learning model and the training data is described, as well as the preprocessing techniques that each textual input is subjected to before its classification into a category. Finally, improvements on the platform are proposed for offering more options and functionalities to the users

Semantic manipulation and business context in big data analytics

Business organisations receive a huge amount of data from many sources every day. These data are known as big data. Since they are mostly unstructured, big data creates a complex problem of how to capture, manage, analyse and then derive meaningful information from them. To deal with the challenges that big data has brought, this research proposes a new technique in big data analytics in the business area to integrate semantically meaningful information relevant to textual queries and business context. To achieve this aim, this study makes three major related contributions. Firstly, the relationship between business processes and strategies is established using the concept of a rule-based inference model via facts and annotations. This relationship is required to determine the importance of a big data query for a business organisation. Secondly, we introduce approaches to determine the significance level of a query, by incorporating the processstrategy relationship, process contributions and priority of business strategies. Thirdly, the proposed data analytic technique embeds business context into the bedrock of data collection and analysis process. The first two contributions were implemented using Python programming language including the Pyke package (Pyke is built in the Python environment and has an artificial intelligence tool for the development of expert systems) and their performances were analysed based on a business use case. The last contribution was implemented mainly in the Hadoop and Java programs. Results show that the first contribution successfully establishes the processstrategy relationship, the second calculates the significance level of a query in relation to a business organisation, while the third reveals the huge impact of query significance level and business context on big data collection and captures deep business insights.Doctor of Philosoph

Quale biblioteca pubblica per il XXI secolo? Modelli e valutazione in una prospettiva comparata

L’obiettivo del progetto di ricerca è stato quello di analizzare, mediante un approccio metodologico comparato, i principali modelli organizzativi e funzionali di biblioteca pubblica sviluppatisi nel panorama internazionale, nel tentativo di individuare le peculiarità della biblioteca pubblica italiana contemporanea. La prima parte del progetto ha previsto la definizione e la comparazione di alcuni dei modelli di biblioteca pubblica più noti in ambito internazionale (public library, médiathèque, biblioteca civica, dreigeteilte Bibliothek, fraktale Bibliothek, Idea Store, Four-spaces model), nati in contesti culturali e sociali storicamente determinati, che si sono evoluti nel tempo e hanno trovato, con i necessari e dovuti adattamenti, spazio e diffusione anche al di fuori dei loro confini cronologici e geografici. La seconda parte, muovendo dal dibattito più recente sull’identità della biblioteca pubblica nel XXI secolo e sulla sua evoluzione, si è concentrata su alcune delle realizzazioni di biblioteca più riuscite in Italia, per individuare tratti distintivi e comuni alle esperienze e ai modelli consolidatisi al di fuori del nostro paese e valutarne funzioni, servizi, risultati e impatto sociale nel contesto di riferimento. Ciò ha permesso di portare alla luce quei fattori contestuali che determinano le cause di successo o di insuccesso di ciascun modello, così da acquisire solidi strumenti di analisi per le biblioteche esistenti e di progettazione per nuove biblioteche

A probabilistic approach to Tweets’ Sentiment Classification

Prior to 2003, mankind generated a total of about 5 Exabyte’s of contents. Now, we generate this amount of contents in about two days! The spread of generic (as Twitter, Facebook or Google+) or specialized (as LinkedIn or Viadeo) social networks allows sharing opinions on different aspects of life every day. Therefore this information is a rich source of data for opinion mining and sentiment analysis. This paper introduces a novel approach to the sentiment analysis based on the Weighted Word Pairs obtained by the use of the Latent Dirichlet Allocation (LDA) approach. The proposed methodology aims at identifying a word-based graphical model for depicting and mining a positive or negative attitude towards a topic. For the evaluation of the proposed approach a challenging scenario has been set: the real-time analysis of tweets. The experimental evaluation shows how the proposed approach is effective and satisfactor