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University​ ​Of​ ​Camerino UNIVERSITY OF CAMERINO ​ ​ ​ ​ School of Science and Technology ​ ​ ​ ​ ​ ​ ​ ​ Degree in Computer Science ​ ​ ​ ​ ​ ​ Software and systems for industries ​ ​ ​ ​ ​ ​ ​ ​ (Class LM-18) ​ ​ A Framework for web presence sentiment ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ analysis Graduand : Supervisor: ​ ​ Andrea Marcelli Prof. Fausto Marcantoni ​ ​ ​ ​ ​ ​ Academic year 2016/2017 ​ ​ ​ ​​ ​​ 2 Abstract (English) The World Wide Web has gone a long way since its early days in development at the CERN Laboratories. Now holding immense relevance in everyday activities, being intertwined with most of the people lives on this planet, its network of links and applications allow users to reach any kind of content they want. The huge quantity of data hosted on the Internet can make the difference in a number of diverse applications, but, most importantly for the scope of this work, can be useful in determining with a certain degree of precision, the polarity of opinions held by communities, users and newspapers alike with respect to a certain individual or business. As a matter of fact, data can hold either a positive or negative meaning when stating something about a topic, or in even in more general terms, as the name of an individual or business can be cited alongside positive or negative content. This activity, called “Sentiment Analysis” has been actively researched upon in several influential papers. This work is based on the framework for Sentiment Analysis created by Bing Liu ​ and cited in several researches. However, it aims at simplifying it by tailoring to a different, more specific context with respect to the more general one that a good general framework such as that developed by Liu should address. This work introduces the concept of “influence score”, that is the strength with which a text is said to be able to impact the reputation of the search subject. By using this companion concept, the simplified framework proposed in this work tries to address the issue of determining, in a naive way, the amount of impact that any content can have on either the individual or business at hand. The framework itself tries to follow a strictly scientific process by first analyzing the problem with use of Data Science principles and then by defining additional framework components and concepts to be used alongside said principles. The companion tool to the framework is developed to provide a first implementation of the concepts by examining results pages (SERPs) obtained by searching the web be means of a search engine in order to analyse each SERPs item separately, to obtain its sentiment and influence score and to provide, overtime, a monitoring functionality for the user. This document takes into account the different items at play that interacts within the framework implementation, such as Search Engines (Chapter 1), Data Science (Chapter 2), Explorative Algorithmic Techniques (Chapter 3), Web Reputation (Chapter 4), The Tool (Chapter 5), finally dwelling on future possibilities and additions to the work in Chapter 6. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 3 (Italiano) Il World Wide Web ha raggiunto uno stadio di sviluppo ed utilizzo estremamente avanzato rispetto a quanto era stato preventivato all’epoca del suo iniziale sviluppo ai laboratori CERN. Difatti, si può affermare che esso abbia raggiunto una notevole importanza nelle attività di tutti i giorni. Essendo le persone della maggior parte dei paesi poste a stretto contatto con esso, la sua rete di collegamenti e le numerose applicazioni native, esso consente ai suoi utenti di sfogliare qualsiasi tipo di contenuto essi vogliono. La grande mole di dati ospitata da Internet può essere impiegata in numerosi ambiti e diverse applicazioni ma, nel contesto di questa ricerca, rimane di maggior interesse la possibilità di utilizzare tali dati al fine di determinare, con un certo grado di precisione, la polarità delle opinioni espresse da comunità online, utenti o perfino giornali web circa un determinato argomento, individuo o business. Difatti, si può affermare che i dati possano essere caratterizzati da un orientamento, sia esso positivo o negativo, sia nel contesto specifico di un argomento di ricerca, che in termini più generali, quando tale soggetto viene citato in contesti caratterizzati da contenuto positivo o negativo. Questo genere di attività, denominata “Analisi del sentimento” (in inglese, Sentiment Analysis) è stata largamente studiata in diversi importanti studi in materia. Il seguente lavoro, si basa sul lavoro proposto da Bing Liu, che introduce ​ ​ un framework per l’analisi del sentimento, citato in numerose ricerche successive. Nonostante sia basato sul lavoro di Liu, il presente lavoro si propone di introdurre un framework semplificato e adattato a un contesto più specifico rispetto a ciò per cui il framework originale era stato inizialmente progettato. In particolare, si introduce il concetto di “punteggio di influenza” , definito come la misura con cui un particolare testo è in grado di influenzare la reputazione del soggetto della ricerca. Utilizzando questo concetto, il framework si propone di determinare, in modo semplice, la quantità di impatto che un determinato contenuto può avere sull’individuo o business in esame. Il framework si basa su di un processo scientifico che prevede una fase di iniziale di analisi dei dati attraverso l’uso dei principi della Scienza dei Dati e la definizione di ulteriori componenti e concetti interni al framework stesso. L’applicazione sviluppata come prima implementazione del framework analizza le pagine risultanti (SERPs) da una ricerca web via motori di ricerca analizzandone ogni elemento separatamente, in maniera tale da ottenerne i relativi valori di sentimento e punteggio di influenza per arricchire l’utente, come risultato di un’analisi continua, di una utile funzionalità di monitoraggio della propria reputazione. Questo documento prende, inoltre, in esame tutte le varie componenti che interagiscono tra loro all’interno dell’implementazione del framework, come i motori di ricerca ( Search Engines, Capitolo 1), la scienza dei dati (Data Science, Capitolo 2), le tecniche esplorative dei contenuti (Explorative Algorithmic Techniques, Chapter 3), reputation online (Web Reputation, Chapter 4), l’applicazione (The Tool, Chapter 5) analizzando, in ultima istanza, le eventuali aggiunte e sviluppi futuri nel Capitolo 6. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 4 Abstract 3 (English) 3 (Italiano) 4 1. Search Engines: finding the needle in the haystack 9 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.1. At the heart of the web 9 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.2. Search Engines 11 ​ ​ ​ ​ 1.2.1. Browsing the past 11 ​ ​ ​ ​ ​ ​ 1.2.1.2 Directories vs. Search Engines (The Yahoo example) 13 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.2.1.3. Other types of search engines 14 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.2.2. The Algorithms and the theoretical framework 15 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.2.2.1. Elements commons to all search engines 15 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.2.2.2. Elements search engines differ in 15 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.2.2.3. General modules of a search engine: a more in depth look 16 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.2.2.4. Crawling and indexing 17 ​ ​ ​ ​ ​ ​ 1.2.2.5. Relevance and popularity 18 ​ ​ ​ ​ ​ ​ 1.2.2.5.1. Relevance 18 ​ ​ 1.2.2.5.1. Popularity 19 ​ ​ 1.2.2.7. Search Engine Ranking Factors 19 ​ ​ ​ ​ ​ ​ ​ ​ 1.2.2.8. Page rank: at Google’s foundation 20 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.2.2.9. Hits 23 ​ ​ Image: hubs and authorities 23 ​ ​ ​ ​ ​ ​ 1.2.2.10. TF-IDF 24 ​ ​ 1.2.2.11. Beyond the algorithms: influencing factors 26 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.3. With many data, come great possibilities 27 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.3.1. Meaningful results: web reputation 28 ​ ​ ​ ​ ​ ​ ​ ​ 1.3.2. What does the “query” say 28 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 1.3.3. An interesting question: is it possible to obtain more information from the ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ returned result set? 29 ​ ​ ​ ​ 2. Data Science: a brief explorations of the principles behind data 31 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 2.1. A scientific approach to data analysis 31 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 2.1.1. An introduction to Data Science 31 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 2.1.2. Data types 32 ​ ​ ​ ​ 2.1.2.1. Organized and unorganized data 32 ​ ​ ​ ​ ​ ​ ​ ​ 2.1.2.2. Quantitative and qualitative data 33 ​ ​ ​ ​ ​ ​ ​ ​ 2.1.2.3. The four levels of data 33 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 2.1.2.3.1. Nominal level 34 ​ ​ ​ ​ 2.1.2.3.2. Ordinal level 34 ​ ​ ​ ​ 2.1.2.3.3. Intervals level 35 ​ ​ ​ ​ 2.1.2.3.4. Ratio level 36 ​ ​ ​ ​ 2.1.2.3.5. Summary 36 ​ ​ 5 2.1.3. Data science: a five steps scientific approach 37 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 2.1.3.1. Posing an interesting question 38 ​ ​ ​ ​ ​ ​ ​ ​ 2.1.3.2. Fetching data 38 ​ ​ ​ ​ 2.1.3.3. Exploring data 38 ​ ​ ​ ​ 2.1.3.4. Modelling data 39 ​ ​ ​ ​ 2.1.3.5. Presenting findings 41 ​ ​ ​ ​ 2.1.4. Applied data science: extracting info on the web 42 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 2.1.4.1. News Analytics 42 ​ ​ ​ ​ 2.1.4.1.1. Algorithms 43 ​ ​ 2.1.4.1.2. Models 44 ​ ​ 2.1.4.1.3. Putting it all together 49 ​ ​ ​ ​ ​ ​ ​ ​ 3. Explorative algorithmic techniques 51 ​ ​ ​ ​ ​ ​ 3.1. Sentiment Analysis 51 ​ ​ ​ ​ 3.1.1. A framework for Sentiment Analysis 52 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 3.1.1.1. Tasks of sentiment analysis 53 ​ ​ ​ ​ ​ ​ ​ ​ 3.2. Influence Score 54 ​ ​ ​ ​ 3.2.1. Defining the problem 55 ​ ​ ​ ​ ​ ​ 3.2.1.1. Additional measures 56 ​ ​ ​ ​ 3.2.1.2. Adjusting the framework 57 ​ ​ ​ ​ ​ ​ 4.1. Meaningful results: web reputation 62 ​ ​ ​ ​ ​ ​ ​ ​ 4.1.2. Web Reputation Management 63 ​ ​ ​ ​ ​ ​ 4.1.2.1. Web Reputation Activities 64 ​ ​ ​ ​ ​ ​ 4.1.2.1. Techniques 66 ​ ​ 4.1.2.2. Page indexing altering techniques 67 ​ ​ ​ ​ ​ ​ ​ ​ 4.1.2.2.1. SEO - Search Engine Optimization 67 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 4.1.2.2.2. When SEO is not enough 68 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 4.1.2.3. Ethics 69 ​ ​ 5. The Companion Tool 70 ​ ​ ​ ​ ​ ​ 5.1. An analysis of the work 70 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 5.1.1. A structural problem analysis 70 ​ ​ ​ ​ ​ ​ ​ ​ 5.1.1.1. Getting the Data 71 ​ ​ ​ ​ ​ ​ 5.1.1.2. Data Exploration 72 ​ ​ ​ ​ 5.1.1.3. Modelling Data 73 ​ ​ ​ ​ 5.1.1.4. Presenting Findings 73 ​ ​ ​ ​ 5.1.2. Tool’s architecture 74 ​ ​ ​ ​ 5.1.2.1. Early development 74 ​ ​ ​ ​ 5.1.2.2. Later stages of development 75 ​ ​ ​ ​ ​ ​ ​ ​ 5.1.2.3.
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