Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Sentiment Analysis of Textual Content in Social Networks. From Hand-Crafted to Deep Learning-Based Models

Aquesta tesi proposa diversos mètodes avançats per analitzar automàticament el contingut textual compartit a les xarxes socials i identificar les opinions, emocions i sentiments a diferents nivells d’anàlisi i en diferents idiomes. Comencem proposant un sistema d’anàlisi de sentiments, anomenat SentiRich, basat en un conjunt ric d’atributs, inclosa la informació extreta de lèxics de sentiments i models de word embedding pre-entrenats. A continuació, proposem un sistema basat en Xarxes Neurals Convolucionals i regressors XGboost per resoldre una sèrie de tasques d’anàlisi de sentiments i emocions a Twitter. Aquestes tasques van des de les tasques típiques d’anàlisi de sentiments fins a determinar automàticament la intensitat d’una emoció (com ara alegria, por, ira, etc.) i la intensitat del sentiment dels autors a partir dels seus tweets. També proposem un nou sistema basat en Deep Learning per solucionar el problema de classificació de les emocions múltiples a Twitter. A més, es va considerar el problema de l’anàlisi del sentiment depenent de l’objectiu. Per a aquest propòsit, proposem un sistema basat en Deep Learning que identifica i extreu l'objectiu dels tweets. Tot i que alguns idiomes, com l’anglès, disposen d’una àmplia gamma de recursos per permetre l’anàlisi del sentiment, a la majoria de llenguatges els hi manca. Per tant, utilitzem la tècnica d'anàlisi de sentiments entre idiomes per desenvolupar un sistema nou, multilingüe i basat en Deep Learning per a llenguatges amb pocs recursos lingüístics. Proposem combinar l’ajuda a la presa de decisions multi-criteri i anàlisis de sentiments per desenvolupar un sistema que permeti als usuaris la possibilitat d’explotar tant les opinions com les seves preferències en el procés de classificació d’alternatives. Finalment, vam aplicar els sistemes desenvolupats al camp de la comunicació de les marques de destinació a través de les xarxes socials. Amb aquesta finalitat, hem recollit tweets de persones locals, visitants i els gabinets oficials de Turisme de diferents destinacions turístiques i es van analitzar les opinions i les emocions compartides en ells. En general, els mètodes proposats en aquesta tesi milloren el rendiment dels enfocaments d’última generació i mostren troballes apassionants.Esta tesis propone varios métodos avanzados para analizar automáticamente el contenido textual compartido en las redes sociales e identificar opiniones, emociones y sentimientos, en diferentes niveles de análisis y en diferentes idiomas. Comenzamos proponiendo un sistema de análisis de sentimientos, llamado SentiRich, que está basado en un conjunto rico de características, que incluyen la información extraída de léxicos de sentimientos y modelos de word embedding previamente entrenados. Luego, proponemos un sistema basado en redes neuronales convolucionales y regresores XGboost para resolver una variedad de tareas de análisis de sentimientos y emociones en Twitter. Estas tareas van desde las típicas tareas de análisis de sentimientos hasta la determinación automática de la intensidad de una emoción (como alegría, miedo, ira, etc.) y la intensidad del sentimiento de los autores de los tweets. También proponemos un novedoso sistema basado en Deep Learning para abordar el problema de clasificación de emociones múltiples en Twitter. Además, consideramos el problema del análisis de sentimientos dependiente del objetivo. Para este propósito, proponemos un sistema basado en Deep Learning que identifica y extrae el objetivo de los tweets. Si bien algunos idiomas, como el inglés, tienen una amplia gama de recursos para permitir el análisis de sentimientos, la mayoría de los idiomas carecen de ellos. Por lo tanto, utilizamos la técnica de Análisis de Sentimiento Inter-lingual para desarrollar un sistema novedoso, multilingüe y basado en Deep Learning para los lenguajes con pocos recursos lingüísticos. Proponemos combinar la Ayuda a la Toma de Decisiones Multi-criterio y el análisis de sentimientos para desarrollar un sistema que brinde a los usuarios la capacidad de explotar las opiniones junto con sus preferencias en el proceso de clasificación de alternativas. Finalmente, aplicamos los sistemas desarrollados al campo de la comunicación de las marcas de destino a través de las redes sociales. Con este fin, recopilamos tweets de personas locales, visitantes, y gabinetes oficiales de Turismo de diferentes destinos turísticos y analizamos las opiniones y las emociones compartidas en ellos. En general, los métodos propuestos en esta tesis mejoran el rendimiento de los enfoques de vanguardia y muestran hallazgos interesa.This thesis proposes several advanced methods to automatically analyse textual content shared on social networks and identify people’ opinions, emotions and feelings at a different level of analysis and in different languages. We start by proposing a sentiment analysis system, called SentiRich, based on a set of rich features, including the information extracted from sentiment lexicons and pre-trained word embedding models. Then, we propose an ensemble system based on Convolutional Neural Networks and XGboost regressors to solve an array of sentiment and emotion analysis tasks on Twitter. These tasks range from the typical sentiment analysis tasks, to automatically determining the intensity of an emotion (such as joy, fear, anger, etc.) and the intensity of sentiment (aka valence) of the authors from their tweets. We also propose a novel Deep Learning-based system to address the multiple emotion classification problem on Twitter. Moreover, we considered the problem of target-dependent sentiment analysis. For this purpose, we propose a Deep Learning-based system that identifies and extracts the target of the tweets. While some languages, such as English, have a vast array of resources to enable sentiment analysis, most low-resource languages lack them. So, we utilise the Cross-lingual Sentiment Analysis technique to develop a novel, multi-lingual and Deep Learning-based system for low resource languages. We propose to combine Multi-Criteria Decision Aid and sentiment analysis to develop a system that gives users the ability to exploit reviews alongside their preferences in the process of alternatives ranking. Finally, we applied the developed systems to the field of communication of destination brands through social networks. To this end, we collected tweets of local people, visitors, and official brand destination offices from different tourist destinations and analysed the opinions and the emotions shared in these tweets

Annual report 2013

Podeu consultar la versió en català a: http://hdl.handle.net/11703/8810

Mobile services for green living

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsUrban cycling is a sustainable transport mode that many cities are promoting. However, few cities are taking advantage of geospatial technologies to represent and analyse behavioural patterns and barriers faced during cycling. This thesis is within the fields of geoinformatics and serious games, and the motivation came from our desire to help both citizens and cities to better understand cyclist behaviour and mobility patterns. We attempted to learn more about the impact of gamified strategies on engagement with cycling, the reasons for choosing between mobile cycling applications and the way such applications would provide commuting information. Furthermore, we explored the potential benefits of offering tools to build decision-making for mobility more transparent, to increase cycling data availability, and to analyse commuting patterns. In general, we found our research useful to enhance green living actions by increasing citizens’ willingness to commute by bicycle or communicating cycling conditions in cities. For urban cycling, data coming from mobile phones can provide a better assessment and enrich the analysis presented in traditional mobility plans. However, the diversity of current mobile applications targeting cyclists does not provide useful data for analysing commuter (inner-city, non-sporting) cycling. Just a few cyclists are adopting these applications as part of their commuting routine, while on the other hand cities are lacking a valuable source of constantly updated cycling information helpful to understand cycling patterns and the role of bicycles in urban transport. This thesis analyses how the incentives of location-based games or geo-games might increase urban cycling engagement and, through this engagement, crowdsource cycling data collection to allow cities to better comprehend cycling patterns. Consequently, the experiment followed a between-groups design to measure the impact of virtual rewards provided by the Cyclist Geo-c application on the levels of intention, satisfaction, and engagement with cycling. Then, to identify the frictions which potentially inhibit bicycle commuting, we analysed the bicycle trips crowdsourced with the geo-game. Our analysis relied on a hexagonal grid of 30-metre cell side to aggregate trip trajectories, calculate the friction intensity and locate the frictions. The thesis reports on the results of an experiment which involved a total of 57 participants in three European cities: M¨unster (Germany), Castell ´o (Spain), and Valletta (Malta). We found participants reported higher satisfaction and engagement with cycling during the experiment in the collaboration condition. However, we did not find a significant impact on the participants’ worldview when it comes to the intentions to start or increase cycling. The results support the use of collaboration-based rewards in the design of game-based applications to promote urban cycling. Furthermore, we validated a procedure to identify not only the cyclists’ preferred streets but also the frictions faced during cycling analysing the crowdsourced trips. We successfully identified 284 places potentially having frictions: 71 in M¨unster, Germany; 70 in Castell ´ o, Spain; and 143 in Valletta, Malta. At such places, participants recorded trip segments at speeds below 5 Km/h indicating a deviation from a hypothetical scenario with a constant cycling speed. This thesis encompasses the cyclist and city perspectives of offering virtual incentives in geo-games and crowdsourcing cycling data collection to better comprehend cycling conditions in cities. We also compiled a set of tools and recommendations for researchers, practitioners, mobile developers, urban planners and cyclist associations interested in fostering sustainable transport and the use of bicycles

On marked declaratives, exclamatives, and discourse particles in Castilian Spanish

This book provides a new perspective on prosodically marked declaratives, wh-exclamatives, and discourse particles in the Madrid variety of Spanish. It argues that some marked forms differ from unmarked forms in that they encode modal evaluations of the at-issue meaning. Two epistemic evaluations that can be shown to be encoded by intonation in Spanish are linguistically encoded surprise, or mirativity, and obviousness. An empirical investigation via an audio-enhanced production experiment finds that mirativity and obviousness are associated with distinct intonational features under constant focus scope, with stances of (dis)agreement showing an impact on obvious declaratives. Wh-exclamatives are found not to differ significantly in intonational marking from neutral declaratives, showing that they need not be miratives. Moreover, we find that intonational marking on different discourse particles in natural dialogue correlates with their meaning contribution without being fully determined by it. In part, these findings quantitatively confirm previous qualitative findings on the meaning of intonational configurations in Madrid Spanish. But they also add new insights on the role intonation plays in the negotiation of commitments and expectations between interlocutors

Controversy trend detection in social media

In this research, we focus on the early prediction of whether topics are likely to generate significant controversy (in the form of social media such as comments, blogs, etc.). Controversy trend detection is important to companies, governments, national security agencies, and marketing groups because it can be used to identify which issues the public is having problems with and develop strategies to remedy them. For example, companies can monitor their press release to find out how the public is reacting and to decide if any additional public relations action is required, social media moderators can moderate discussions if the discussions start becoming abusive and getting out of control, and governmental agencies can monitor their public policies and make adjustments to the policies to address any public concerns. An algorithm was developed to predict controversy trends by taking into account sentiment expressed in comments, burstiness of comments, and controversy score. To train and test the algorithm, an annotated corpus was developed consisting of 728 news articles and over 500,000 comments on these articles made by viewers from CNN.com. This study achieved an average F-score of 71.3% across all time spans in detection of controversial versus non-controversial topics. The results suggest that it is possible for early prediction of controversy trends leveraging social media

Learning Analytics para el personal académico

Hoy en día, el Learning Analytics (LA), siendo una disciplina relacionada con la ciencia de datos, se ha convertido en una herramienta muy útil para la gestión universitaria. En nuestro caso, proponemos un sistema de LA para ayudar al personal académico en la gestión de grados universitarios. El objetivo principal de nuestro sistema de LA es recopilar información de diferentes fuentes, que comúnmente están disponibles para el personal universitario, y facilitar su comprensión gracias a las visualizaciones y predicciones. En particular, proponemos técnicas de aprendizaje automático para extraer tres indicadores clave para la gestión y la evaluación de la calidad de los títulos universitarios, y que son difíciles de analizar con herramientas estándar. Este sistema de LA se presenta como un conjunto de paneles fáciles de usar dirigidos a ayudar en el proceso de toma de decisiones para gerentes universitarios, decanos, directores o coordinadores de cursos.The field of Learning Analytics (LA), as a data science related discipline, has become a very useful tool for auditing and managing. In this work we propose a LA system for academic personnel to help them in university degrees management. The main goal of our LA system is to gather information from different sources –which are commonly available for university personnel– and make it easier to understand by means of prediction and visualization. In particular, we propose machine learning techniques to extract three key indicators for the management and quality assurance of the university degrees, which are difficult to analyze with off-the-shelf tools. This LA system is presented as a set of user-friendly dashboards addressed to help in the decision making process for university managers, such faculty deans, headmasters or course coordinators.Esta investigación ha recibido soporte parcial de la Universitat de Barcelona: Convocatòria d’Ajuts a la Recerca en Docència Universitària de l’Institut de Ciències de l’Educació de la Universitat de Barcelona REDICE-18 y Proyecto 2014PID-UB/068