Analyse de l’image de marque sur le Web 2.0

Analyse of entities representation over the Web 2.0Every day, millions of people publish their views on Web 2.0 (social networks,blogs, etc.). These comments focus on subjects as diverse as news, politics,sports scores, consumer objects, etc. The accumulation and agglomerationof these notices on an entity (be it a product, a company or a public entity) givebirth to the brand image of that entity. Internet has become in recent years aprivileged place for the emergence and dissemination of opinions and puttingWeb 2.0 at the head of observatories of opinions. The latter being a means ofaccessing the knowledge of the opinion of the world population.The image is here understood as the idea that a person or a group of peopleis that entity. This idea carries a priori on a particular subject and is onlyvalid in context for a given time. This perceived image is different from theentity initially wanted to broadcast (eg via a communication campaign). Moreover,in reality, there are several images in the end living together in parallel onthe network, each specific to a community and all evolve differently over time(imagine how would be perceived in each camp together two politicians edgesopposite). Finally, in addition to the controversy caused by the voluntary behaviorof some entities to attract attention (think of the declarations required orshocking). It also happens that the dissemination of an image beyond the frameworkthat governed the and sometimes turns against the entity (for example,« marriage for all » became « the demonstration for all »). The views expressedthen are so many clues to understand the logic of construction and evolution ofthese images. The aim is to be able to know what we are talking about and howwe talk with filigree opportunity to know who is speaking.viiIn this thesis we propose to use several simple supervised statistical automaticmethods to monitor entity’s online reputation based on textual contentsmentioning it. More precisely we look the most important contents and theirsauthors (from a reputation manager point-of-view). We introduce an optimizationprocess allowing us to enrich the data using a simulated relevance feedback(without any human involvement). We also compare content contextualizationmethod using information retrieval and automatic summarization methods.Wealso propose a reflection and a new approach to model online reputation, improveand evaluate reputation monitoring methods using Partial Least SquaresPath Modelling (PLS-PM). In designing the system, we wanted to address localand global context of the reputation. That is to say the features can explain thedecision and the correlation betweens topics and reputation. The goal of ourwork was to propose a different way to combine usual methods and featuresthat may render reputation monitoring systems more accurate than the existingones. We evaluate and compare our systems using state of the art frameworks: Imagiweb and RepLab. The performances of our proposals are comparableto the state of the art. In addition, the fact that we provide reputation modelsmake our methods even more attractive for reputation manager or scientistsfrom various fields.Image sur le web : analyse de la dynamique des images sur le Web 2.0. En plus d’être un moyen d’accès à la connaissance, Internet est devenu en quelques années un lieu privilégié pour l’apparition et la diffusion d’opinions.Chaque jour, des millions d’individus publient leurs avis sur le Web 2.0 (réseaux sociaux, blogs, etc.). Ces commentaires portent sur des sujets aussi variés que l’actualité, la politique, les résultats sportifs, biens culturels, des objets de consommation, etc. L’amoncellement et l’agglomération de ces avis publiés sur une entité (qu’il s’agisse d’un produit, une entreprise ou une personnalité publique)donnent naissance à l’image de marque de cette entité.L’image d’une entité est ici comprise comme l’idée qu’une personne ou qu’un groupe de personnes se fait de cette entité. Cette idée porte a priori sur un sujet particulier et n’est valable que dans un contexte, à un instant donné.Cette image perçue est par nature différente de celle que l’entité souhaitait initialement diffuser (par exemple via une campagne de communication). De plus,dans la réalité, il existe au final plusieurs images qui cohabitent en parallèle sur le réseau, chacune propre à une communauté et toutes évoluant différemment au fil du temps (imaginons comment serait perçu dans chaque camp le rapprochement de deux hommes politiques de bords opposés). Enfin, en plus des polémiques volontairement provoquées par le comportement de certaines entités en vue d’attirer l’attention sur elles (pensons aux tenues ou déclarations choquantes), il arrive également que la diffusion d’une image dépasse le cadre qui la régissait et même parfois se retourne contre l’entité (par exemple, «le mariage pour tous» devenu « la manif pour tous »). Les opinions exprimées constituent alors autant d’indices permettant de comprendre la logique de construction et d’évolution de ces images. Ce travail d’analyse est jusqu’à présent confié à des spécialistes de l’e-communication qui monnaient leur subjectivité. Ces derniers ne peuvent considérer qu’un volume restreint d’information et ne sont que rarement d’accord entre eux. Dans cette thèse, nous proposons d’utiliser différentes méthodes automatiques, statistiques, supervisées et d’une faible complexité permettant d’analyser et représenter l’image de marque d’entité à partir de contenus textuels les mentionnant. Plus spécifiquement, nous cherchons à identifier les contenus(ainsi que leurs auteurs) qui sont les plus préjudiciables à l’image de marque d’une entité. Nous introduisons un processus d’optimisation automatique de ces méthodes automatiques permettant d’enrichir les données en utilisant un retour de pertinence simulé (sans qu’aucune action de la part de l’entité concernée ne soit nécessaire). Nous comparer également plusieurs approches de contextualisation de messages courts à partir de méthodes de recherche d’information et de résumé automatique. Nous tirons également parti d’algorithmes de modélisation(tels que la Régression des moindres carrés partiels), dans le cadre d’une modélisation conceptuelle de l’image de marque, pour améliorer nos systèmes automatiques de catégorisation de documents textuels. Ces méthodes de modélisation et notamment les représentations des corrélations entre les différents concepts que nous manipulons nous permettent de représenter d’une part, le contexte thématique d’une requête de l’entité et d’autre, le contexte général de son image de marque. Nous expérimentons l’utilisation et la combinaison de différentes sources d’information générales représentant les grands types d’information auxquels nous sommes confrontés sur internet : de long les contenus objectifs rédigés à des informatives, les contenus brefs générés par les utilisateurs visant à partager des opinions. Nous évaluons nos approches en utilisant deux collections de données, la première est celle constituée dans le cadre du projet Imagiweb, la seconde est la collection de référence sur le sujet : CLEFRepLa

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation

SpeakerLDA: Discovering Topics in Transcribed Multi-Speaker Audio Contents

ABSTRACT Topic models such as Latent Dirichlet Allocation (LDA

Tracking public opinion on social media

The increasing popularity of social media has changed the web from a static repository of information into a dynamic forum with continuously changing information. Social media platforms has given the capability to people expressing and sharing their thoughts and opinions on the web in a very simple way. The so-called User Generated Content is a good source of users opinion and mining it can be very useful for a wide variety of applications that require understanding the public opinion about a concept. For example, enterprises can capture the negative or positive opinions of customers about their services or products and improve their quality accordingly. The dynamic nature of social media with the constantly changing vocabulary, makes developing tools that can automatically track public opinion a challenge. To help users better understand public opinion towards an entity or a topic, it is important to: a) find the related documents and the sentiment polarity expressed in them; b) identify the important time intervals where there is a change in the opinion; c) identify the causes of the opinion change; d) estimate the number of people that have a certain opinion about the entity; and e) measure the impact of public opinion towards the entity. In this thesis we focus on the problem of tracking public opinion on social media and we propose and develop methods to address the different subproblems. First, we analyse the topical distribution of tweets to determine the number of topics that are discussed in a single tweet. Next, we propose a topic specific stylistic method to retrieve tweets that are relevant to a topic and also express opinion about it. Then, we explore the effectiveness of time series methodologies to track and forecast the evolution of sentiment towards a specific topic over time. In addition, we propose the LDA & KL-divergence approach to extract and rank the likely causes of sentiment spikes. We create a test collection that can be used to evaluate methodologies in ranking the likely reasons of sentiment spikes. To estimate the number of people that have a certain opinion about an entity, we propose an approach that uses pre-publication and post- publication features extracted from news posts and users' comments respectively. Finally, we propose an approach that propagates sentiment signals to measure the impact of public opinion towards the entity's reputation. We evaluate our proposed methods on standard evaluation collections and provide evidence that the proposed methods improve the performance of the state-of-the-art approaches on tracking public opinion on social media

Social-media monitoring for cold-start recommendations

Generating personalized movie recommendations to users is a problem that most commonly relies on user-movie ratings. These ratings are generally used either to understand the user preferences or to recommend movies that users with similar rating patterns have rated highly. However, movie recommenders are often subject to the Cold-Start problem: new movies have not been rated by anyone, so, they will not be recommended to anyone; likewise, the preferences of new users who have not rated any movie cannot be learned. In parallel, Social-Media platforms, such as Twitter, collect great amounts of user feedback on movies, as these are very popular nowadays. This thesis proposes to explore feedback shared on Twitter to predict the popularity of new movies and show how it can be used to tackle the Cold-Start problem. It also proposes, at a finer grain, to explore the reputation of directors and actors on IMDb to tackle the Cold-Start problem. To assess these aspects, a Reputation-enhanced Recommendation Algorithm is implemented and evaluated on a crawled IMDb dataset with previous user ratings of old movies,together with Twitter data crawled from January 2014 to March 2014, to recommend 60 movies affected by the Cold-Start problem. Twitter revealed to be a strong reputation predictor, and the Reputation-enhanced Recommendation Algorithm improved over several baseline methods. Additionally, the algorithm also proved to be useful when recommending movies in an extreme Cold-Start scenario, where both new movies and users are affected by the Cold-Start problem