IWS Novembre 2015 [Results of project]

Sentiment Analysis & Linked Open Data

Introduzione alla Sentiment Analysis partendo dalle parole di Tim Berners-Lee e giungendo al Sentiment Mapping.

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Evoluzione del Web

Far conoscere la storia di Internet dalle origini fino ai giorni nostri

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Basi di progettazione di un sito web

Fornire una breve panoramica sulle basi di progettazione di siti web con riferimenti al web 2.0 e ai social networks

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Sample of Learning Path IWS 2015

Testing and analysis of technical next steps

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TBL at TED

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20 anni fa, TIm Berners-Lee inventò il World Wide Web. Per il suo prossimo progetto, sta lavorando alla creazione di un web pensato in funz…

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Sitografia Completa Sentiment Analysis & Social Media

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Sitografia finale del gruppo 4.

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CMS

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Descrizione delle applicazioni, dei vantaggi, delle funzionalità e strutture dei vari CMS esistenti.

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The Jitsy videobridge is back

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Published on Dec 7, 2013 A quick demo for the https://meet.jit.si service, Jitsi Videobridge (https://jitsi.org/videobridge), and colibri.…

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BLAB antother VideoChat

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BLAB is another videochat server that allows for creating VideoRooms

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CMSes Comparison

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Breve resoconto sulle strategie per individuare i CMS più utili a seconda dei diversi scopi di impiego, che si muove attraverso la comparaz…

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What WebRTC actually is? How will change the WorldWideWeb?

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Pubblicato il 19 mar 2014 What is WebRTC all about? Why are people so excited about this activity? What work is the IETF doing within the …

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Jitsi - one Open Source VideoChat Tool

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This is the demo version of the VideoChat Jitsi. It is possible to insert it into a Learnig Path to start a discussion panel in a certain p…

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Presentazione Gruppo 1

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Presentazione Gruppo 1

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Sentiment Analysis: Nuccio e Giacomo parte 3

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«Dati che possono essere liberamente utilizzati, riutilizzati e redistribuiti, con la sola limitazione– al massimo – della richiesta di att…

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Seo

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SEO: introduction & tools

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Cosa è la SEO

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SEO e Algoritmi Google

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Sentiment Analysis. Oriana Corvaglia e Maria Antonietta Scuccimarra. Parte 2

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DBpedia

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La privacy sul web

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Informazioni generali sulla privacy e sulle modalità per proteggerla.

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Sentiment Analysis. Oriana Corvaglia e Maria Antonietta Scuccimarra. Parte 1

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Lavoro sui Linked Open Data e il DBpedia

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Applicazioni della Sentiment Analysis nei Social Media

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La Sentiment Analysis si applica a svariati campi: in questo lavoro si indaga il suo utilizzo nell'e-commerce e nei Social Media, per poi a…

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HTML:cos'è?

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Brevissimo video di presentazione di HTML

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Infografica Open Data

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Infografica riassuntiva sui concetti cardine del modello Open Data

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Ramon Navarro Bosch - Plone 5 and how to use machine learning with it.

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Pubblicato il 07 ago 2015 Ramon Navarro Bosch - Plone 5 and how to use machine learning with it. [EuroPython 2015] [22 July 2015]

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Plone 4: an introduction to CMS

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An Introduction to the Plone Content Management System currently used by UCLA employees for Campus Portal websites.

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Introduction to CMS Plone

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An very basic introduction to Content Management using Plone

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Privacy e web marketing: attenzione ai flag pre-impostati

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Articolo che tratta la protezione dei dati nell'impostazione e nella pre-impostazione dei flag

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Privacy Cookie: la normativa per i siti web

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Video che tratta la normativa per i siti web riguardante i cookie.

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Privacy: Due terzi dei siti web violano la privacy. Cosa possiamo fare?

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Slide relative alla privacy sul web ( sanzioni, informativa/ consenso) rivolte alle aziende e ai proprietari di siti web, oltre che agli u…

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Sentiment Analysis of Social Media Texts Part 1

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Automatically detecting sentiment of product reviews, blogs, tweets, and SMS messages has attracted extensive interest from both the academ…

Automatically detecting sentiment of product reviews, blogs, tweets, and SMS messages has attracted extensive interest from both the academia and industry. It has a number of applications, including: tracking sentiment towards products, movies, politicians, etc.; improving customer relation models; detecting happiness and well-being; and improving automatic dialogue systems. In this tutorial, we will describe how you can create a state-of-the-art sentiment analysis system, with a focus on social media posts. We begin with an introduction to sentiment analysis and its various forms: term level, message level, document level, and aspect level. We will describe how sentiment analysis systems are evaluated, especially through recent SemEval shared tasks: Sentiment Analysis of Twitter (SemEval-2013 Task 2, SemEval 2014-Task 9) and Aspect Based Sentiment Analysis (SemEval-2014 Task 4). We will give an overview of the best sentiment analysis systems at this point of time, including those that are conventional statistical systems as well as those using deep learning approaches. We will describe in detail the NRC-Canada systems, which were the overall best performing systems in all three SemEval competitions listed above. These are simple lexical- and sentiment-lexicon features based systems, which are relatively easy to re-implement. We will discuss features that had the most impact (those derived from sentiment lexicons and negation handling). We will present how large tweet-specific sentiment lexicons can be automatically generated and evaluated. We will also show how negation impacts sentiment differently depending on whether the scope of the negation is positive or negative. Finally, we will flesh out limitations of current approaches and promising future directions. Instructors: Saif M. Mohammad, Researcher, National Research Council Canada Saif Mohammad is a Research Officer at the National Research Council Canada. His research interests are in Computational Linguistics, especially Lexical Semantics. He develops computational models for sentiment analysis, emotion detection, semantic distance, and lexical-semantic relations such as word-pair antonymy. Xiaodan Zhu, Researcher, National Research Council Canada Xiaodan Zhu is a Research Officer at the National Research Council Canada. His research interests are in Natural Language Processing, Spoken Language Understanding, and Machine Learning. His recent work focuses on sentiment analysis, emotion detection, speech summarization, and deep learning. The instructors, along with Svetlana Kiritchenko, developed the NRC-Canada Sentiment Analysis System, which was the top-performing system in recent SemEval shared-task competitions (SemEval-2013, Task 2, SemEval-2014 Task 9, and SemEval-2014 Task 4).