109 research outputs found

    Contextual Social Networking

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    The thesis centers around the multi-faceted research question of how contexts may be detected and derived that can be used for new context aware Social Networking services and for improving the usefulness of existing Social Networking services, giving rise to the notion of Contextual Social Networking. In a first foundational part, we characterize the closely related fields of Contextual-, Mobile-, and Decentralized Social Networking using different methods and focusing on different detailed aspects. A second part focuses on the question of how short-term and long-term social contexts as especially interesting forms of context for Social Networking may be derived. We focus on NLP based methods for the characterization of social relations as a typical form of long-term social contexts and on Mobile Social Signal Processing methods for deriving short-term social contexts on the basis of geometry of interaction and audio. We furthermore investigate, how personal social agents may combine such social context elements on various levels of abstraction. The third part discusses new and improved context aware Social Networking service concepts. We investigate special forms of awareness services, new forms of social information retrieval, social recommender systems, context aware privacy concepts and services and platforms supporting Open Innovation and creative processes. This version of the thesis does not contain the included publications because of copyrights of the journals etc. Contact in terms of the version with all included publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]

    A knowledge regularized hierarchical approach for emotion cause analysis

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    Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure

    Combining granularity-based topic-dependent and topic-independent evidences for opinion detection

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    Fouille des opinion, une sous-discipline dans la recherche d'information (IR) et la linguistique computationnelle, fait référence aux techniques de calcul pour l'extraction, la classification, la compréhension et l'évaluation des opinions exprimées par diverses sources de nouvelles en ligne, social commentaires des médias, et tout autre contenu généré par l'utilisateur. Il est également connu par de nombreux autres termes comme trouver l'opinion, la détection d'opinion, l'analyse des sentiments, la classification sentiment, de détection de polarité, etc. Définition dans le contexte plus spécifique et plus simple, fouille des opinion est la tâche de récupération des opinions contre son besoin aussi exprimé par l'utilisateur sous la forme d'une requête. Il y a de nombreux problèmes et défis liés à l'activité fouille des opinion. Dans cette thèse, nous nous concentrons sur quelques problèmes d'analyse d'opinion. L'un des défis majeurs de fouille des opinion est de trouver des opinions concernant spécifiquement le sujet donné (requête). Un document peut contenir des informations sur de nombreux sujets à la fois et il est possible qu'elle contienne opiniâtre texte sur chacun des sujet ou sur seulement quelques-uns. Par conséquent, il devient très important de choisir les segments du document pertinentes à sujet avec leurs opinions correspondantes. Nous abordons ce problème sur deux niveaux de granularité, des phrases et des passages. Dans notre première approche de niveau de phrase, nous utilisons des relations sémantiques de WordNet pour trouver cette association entre sujet et opinion. Dans notre deuxième approche pour le niveau de passage, nous utilisons plus robuste modèle de RI i.e. la language modèle de se concentrer sur ce problème. L'idée de base derrière les deux contributions pour l'association d'opinion-sujet est que si un document contient plus segments textuels (phrases ou passages) opiniâtre et pertinentes à sujet, il est plus opiniâtre qu'un document avec moins segments textuels opiniâtre et pertinentes. La plupart des approches d'apprentissage-machine basée à fouille des opinion sont dépendants du domaine i.e. leurs performances varient d'un domaine à d'autre. D'autre part, une approche indépendant de domaine ou un sujet est plus généralisée et peut maintenir son efficacité dans différents domaines. Cependant, les approches indépendant de domaine souffrent de mauvaises performances en général. C'est un grand défi dans le domaine de fouille des opinion à développer une approche qui est plus efficace et généralisé. Nos contributions de cette thèse incluent le développement d'une approche qui utilise de simples fonctions heuristiques pour trouver des documents opiniâtre. Fouille des opinion basée entité devient très populaire parmi les chercheurs de la communauté IR. Il vise à identifier les entités pertinentes pour un sujet donné et d'en extraire les opinions qui leur sont associées à partir d'un ensemble de documents textuels. Toutefois, l'identification et la détermination de la pertinence des entités est déjà une tâche difficile. Nous proposons un système qui prend en compte à la fois l'information de l'article de nouvelles en cours ainsi que des articles antérieurs pertinents afin de détecter les entités les plus importantes dans les nouvelles actuelles. En plus de cela, nous présentons également notre cadre d'analyse d'opinion et tâches relieés. Ce cadre est basée sur les évidences contents et les évidences sociales de la blogosphère pour les tâches de trouver des opinions, de prévision et d'avis de classement multidimensionnel. Cette contribution d'prématurée pose les bases pour nos travaux futurs. L'évaluation de nos méthodes comprennent l'utilisation de TREC 2006 Blog collection et de TREC Novelty track 2004 collection. La plupart des évaluations ont été réalisées dans le cadre de TREC Blog track.Opinion mining is a sub-discipline within Information Retrieval (IR) and Computational Linguistics. It refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online sources like news articles, social media comments, and other user-generated content. It is also known by many other terms like opinion finding, opinion detection, sentiment analysis, sentiment classification, polarity detection, etc. Defining in more specific and simpler context, opinion mining is the task of retrieving opinions on an issue as expressed by the user in the form of a query. There are many problems and challenges associated with the field of opinion mining. In this thesis, we focus on some major problems of opinion mining

    Quantitative Assessment of Factors in Sentiment Analysis

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    Sentiment can be defined as a tendency to experience certain emotions in relation to a particular object or person. Sentiment may be expressed in writing, in which case determining that sentiment algorithmically is known as sentiment analysis. Sentiment analysis is often applied to Internet texts such as product reviews, websites, blogs, or tweets, where automatically determining published feeling towards a product, or service is very useful to marketers or opinion analysts. The main goal of sentiment analysis is to identify the polarity of natural language text. This thesis sets out to examine quantitatively the factors that have an effect on sentiment analysis. The factors that are commonly used in sentiment analysis are text features, sentiment lexica or resources, and the machine learning algorithms employed. The main aim of this thesis is to investigate systematically the interaction between sentiment analysis factors and machine learning algorithms in order to improve sentiment analysis performance as compared to the opinions of human assessors. A software system known as TJP was designed and developed to support this investigation. The research reported here has three main parts. Firstly, the role of data pre-processing was investigated with TJP using a combination of features together with publically available datasets. This considers the relationship and relative importance of superficial text features such as emoticons, n-grams, negations, hashtags, repeated letters, special characters, slang, and stopwords. The resulting statistical analysis suggests that a combination of all of these features achieves better accuracy with the dataset, and had a considerable effect on system performance. Secondly, the effect of human marked up training data was considered, since this is required by supervised machine learning algorithms. The results gained from TJP suggest that training data greatly augments sentiment analysis performance. However, the combination of training data and sentiment lexica seems to provide optimal performance. Nevertheless, one particular sentiment lexicon, AFINN, contributed better than others in the absence of training data, and therefore would be appropriate for unsupervised approaches to sentiment analysis. Finally, the performance of two sophisticated ensemble machine learning algorithms was investigated. Both the Arbiter Tree and Combiner Tree were chosen since neither of them has previously been used with sentiment analysis. The objective here was to demonstrate their applicability and effectiveness compared to that of the leading single machine learning algorithms, Naïve Bayes, and Support Vector Machines. The results showed that whilst either can be applied to sentiment analysis, the Arbiter Tree ensemble algorithm achieved better accuracy performance than either the Combiner Tree or any single machine learning algorithm

    Deep Learning for Text Style Transfer: A Survey

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    Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. We also provide discussions on a variety of important topics regarding the future development of this task. Our curated paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_SurveyComment: Computational Linguistics Journal 202

    Age prediction of Spanish-speaking Twitter users

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    Incluye bibliografía y anexos.Incluye archivos complementarios.La predicción de la edad en la red social Twitter surge como necesidad para el mejoramiento de herramientas como pueden ser el marketing online, así como para colaborar en la detección de pedofilia en la red social, identificando a los usuarios que fingen ser menores de edad mediante el uso de perfiles falsos. En el presente trabajo se analizan diferentes soluciones a este problema, prediciendo el rango de edad de una persona a partir de una colección de textos cortos escrita por la misma. Se analizan tres tipos de atributos: metadatos del usuario, atributos de estilometría sobre el texto de los tuits y atributos resultantes de la aplicación de técnicas de Procesamiento de Lenguaje Natural sobre tuits, así como listas de suscripción las cuales contienen información acerca de los intereses del usuario. También se incluyen una serie de atributos que modelan la vinculación del perfil de Twitter con otras redes sociales. Dichos atributos recolectados son posteriormente utilizados para entrenar los modelos de Aprendizaje Automático, con el fin de predecir la edad de los usuarios y así proceder a clasificarlos en los rangos etarios definidos. Finalmente se realizó una serie de experimentos con distintos set de datos y algoritmos. Los resultados experimentales muestran que los atributos extraídos constituyen un elemento muy útil a la hora de detectar la edad de los usuarios

    Sentence-level sentiment tagging across different domains and genres

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    The demand for information about sentiment expressed in texts has stimulated a growing interest into automatic sentiment analysis in Natural Language Processing (NLP). This dissertation is motivated by an unmet need for high-performance domain-independent sentiment taggers and by pressing theoretical questions in NLP, where the exploration of limitations of specific approaches, as well as synergies between them, remain practically unaddressed. This study focuses on sentiment tagging at the sentence level and covers four genres: news, blogs, movie reviews, and product reviews. It draws comparisons between sentiment annotation at different linguistic levels (words, sentences, and texts) and highlights the key differences between supervised machine learning methods that rely on annotated corpora (corpus-based, CBA) and lexicon-based approaches (LBA) to sentiment tagging. Exploring the performance of supervised corpus-based approach to sentiment tagging, this study highlights the strong domain-dependence of the CBA. I present the development of LBA approaches based on general lexicons, such as WordNet, as a potential solution to the domain portability problem. A system for sentiment marker extraction from WordNet's relations and glosses is developed and used to acquire lists for a lexicon-based system for sentiment annotation at the sentence and text levels. It demonstrates that LBA's performance across domains is more stable than that of CBA. Finally, the study proposes an integration of LBA and CBA in an ensemble of classifiers using a precision-based voting technique that allows the ensemble system to incorporate the best features of both CBA and LBA. This combined approach outperforms both base learners and provides a promising solution to the domain-adaptation problem. The study contributes to NLP (1) by developing algorithms for automatic acquisition of sentiment-laden words from dictionary definitions; (2) by conducting a systematic study of approaches to sentiment classification and of factors affecting their performance; (3) by refining the lexicon-based approach by introducing valence shifter handling and parse tree information; and (4) by development of the combined, CBA/LBA approach that brings together the strengths of the two approaches and allows domain-adaptation with limited amounts of labeled training data
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