1,720,998 research outputs found
Monitoring of yeast microflora during Gragnano DOC (Campania Region, Italy) winemaking and selection of autochthonous Saccharomyces cerevisiae strains.
No full textStartle disease: an avoidable cause of sudden infant death [letter]
No full textStartle disease: an avoidable cause of sudden infant deat
A DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS
Full text linkLa Sentiment Analysis si riferisce alla analisi qualitativa volta ad identificare e classificare opinioni contenute in frasi e testi, allo scopo di stabilire lo “stato d’animo” dell’autore rispetto ad un particolare argomento o prodotto, e di determinare se tale stato è di fatto positivo, negativo oppure neutrale. Le opinioni espresse in un testo, come ad esempio giudizi, sentimenti ed emozioni, sono di recente diventate oggetto di studio e di ricerca sia in ambito accademico che industriale. Sfortunatamente la comprensione del linguaggio, applicata a commenti di utenti, è un attività estremamente complessa per una macchina, specialmente se ci si riferisce ai contesti dei moderni social network. Le modalità in cui le persone si esprimono in linguaggio naturale, sono molteplici, e l’utilizzo “informale” della lingua adottato tipicamente nei social netowrks, genera frasi spesso dense di errori, modi di dire (slang), costrutti sintattici ”personalizzati”, o anche frasi arricchite da caratteri speciali (come l’hashtag in Twitter), il che complica notevolmente l’analisi. Recentemente, le tecniche di Deep Learning, stanno emergendo nel panorama del machine learning, come un modello computazionale che può essere adoperato con efficacia per scoprire relazioni semantiche complesse, all’interno di un testo, anche senza la necessità di dover individuare a priori caratteristiche (features) di tali relazioni. Questi approcci hanno migliorato l’attuale stato dell’arte in diversi settori della Sentiment Analysis, come ad esempio la classificazione di frasi o di documenti, l’apprendimento basato su lexicon, fino ad arrivare alla analisi di fenomeni complessi come il cyber bullismo.
I contributi di questa tesi sono di due tipi. Il primo contributo fornito, relativo ad aspetti generali di Sentiment Analysis, riguarda la proposta di un modello di rete neurale semi supervisionata, basato sulle reti di tipo Deep Belief, in grado di affrontare l’incertezza dei dati insita nelle frasi testuali, con particolare riferimento alla lingua italiana. Il modello proposto è stato testato rispetto a diversi datasets presi dalla letteratura di riferimento, composti da testi relativi a critiche cinematografiche, adottando una rappresentazione dell’informazione basata su vettori (Word2Vec) ed introducendo anche metodi derivati dal campo del Natural Language Processing (NLP). Il secondo contributo fornito in questa tesi, partendo dall’assunto che il cyber bullismo può essere considerato come un caso particolare di Sentiment Analysis, propone un approccio non supervisionato alla rilevazione automatica di tracce di cyber bullismo all’interno di social networks, basato sia su di una rete neurale di tipo GHSOM (Growing Hierarchical Self Organizing Map), sia su di un modello di caratteristiche (features) predefinito. Il modello non supervisionato proposto dimostra di raggiungere comunque risultati interessanti rispetto ai tipici modelli supervisionati, applicati solitamente in questo ambito.Sentiment Analysis refers to the process of computationally identifying and categorizing opinions expressed in a piece of text, in order to determine whether the writer’s attitude towards a particular topic or product is positive, negative, or even neutral. The views expressed and its related concepts, such as feelings, judgments, and emotions have become recently a subject of study and research in both academic and industrial areas. Unfortunately language comprehension of user comments, especially in social networks, is inherently complex to computers. The ways in which humans express themselves with natural language are nearly unlimited and informal texts is riddled with typos, misspellings, badly set up syntactic constructions and also specific symbols (e.g. hashtags in Twitter) which exponentially complicate this task. Recently, deep learning approaches are emerging as powerful computational models that discover intricate semantic representations of texts automatically from data without hand-made feature engineering. These approaches have improved the state-of-the-art in many Sentiment Analysis tasks including sentiment classification of sentences or documents, sentiment lexicon learning and also in more complex problems as cyber bullying detection.
The contributions of this work are twofold. First, related to the general Sentiment Analysis problem, we propose a semi-supervised neural network model, based on Deep Belief Networks, able to deal with data uncertainty for text sentences in Italian language. We test this model against some datasets from literature related to movie reviews, adopting a vectorized representation of text (Word2Vec) and exploiting methods from Natural Language Processing (NLP) pre-processing.
Second, assuming that the cyber bullying phenomenon can be treated as a particular Sentiment Analysis problem, we propose an unsupervised approach to automatic cyber bullying detection in social networks, based both on Growing Hierarchical Self Organizing Map (GHSOM) and on a new specific features model, showing that our solution can achieve interesting results, respect to classical supervised approaches
Production of fermented chestnut purees by lactic acid bacteria
No full textThe objective of this study was to develop a new chestnut-based puree, in order to seasonally adjust the offer and use the surplus of undersized production, providing, at the same time, a response to the growing demand for healthy and environmentally friendly products. Broken dried chestnuts have been employed to prepare purees to be fermented with six different strains of Lactobacillus (Lb.) rhamnosus and Lactobacillus casei. The fermented purees were characterized by a technological and sensorial point of view, while the employed strains were tested for their probiotic potential. Conventional in vitro tests have indicated the six lactobacilli strains as promising probiotic candidates; moreover, being the strains able to grow and to survive in chestnut puree at a population level higher than 8 log10 CFU/mL along 40 days of storage at 4 °C, the bases for the pro- duction of a new food, lactose-free and with reduced fat content, have been laid
A Deep Learning Approach to Deal with Data Uncertainty in Sentiment Analysis
No full textSentiment Analysis refers to the process of computationally identifying and categorizing opinions expressed in a piece of text, in order to determine whether the writer’s attitude towards a particular topic or product is positive, negative, or even neutral. Recently, deep learning approaches emerge as powerful computational models that discover intricate semantic representations of texts automatically from data without hand-made feature engineering. These approaches have improved the state-of-the-art in many Sentiment Analysis tasks including sentiment classification of sentences or documents. In this paper we propose a semi-supervised neural network model, based on Deep Belief Networks, able to deal with data uncertainty for text sentences and adopting the Italian language as a reference language. We test this model against some datasets from literature related to movie reviews, adopting a vectorized representation of text and exploiting methods from Natural Language Processing (NLP) pre-processing
A novel insertion mutation (A(169i)) in the CLN1 gene is associated with infantile neuronal ceroid lipofuscinosis in an Italian patient
No full textInfantile neuronal ceroid lipofuscinosis (INCL) is a progressive encephalopathy characterized by psychomotor deterioration, early visual loss, and an evanishing EEG. Mutations in the CLN1 gene encoding palmitoyl-protein thioesterase (ppt) have been reported in all Finnish INCL patients and in several non-Finnish North European patients. No cases have been contributed from the Mediterranean area thus far. We identified a single adenine insertion at nucleotide position 169 (A(169i)) in the CLN1 gene in a family in which the proband suffered from an INCL-like syndrome. The novel mutation was homozygous in blood from the proband, heterozygous in his healthy parents, and not found in control alleles, The mutation leads to an early stop codon resulting in an abnormal and truncated ppt protein. Our observations provide the first molecular characterization of an Italian INCL patient and expand the list of the known defects in INCL. (C) 1998 Academic Press
Efficacy of very high molecular weight hyaluronic acid intra-articular injections inankle and knee in haemophilic patients: clinical and MRI evaluation
No full textGoing Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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