72 research outputs found

    Modèles de langues pour la détection d'opinions dans les blogs

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    Cet article décrit une approche de recherche de documents pertinents vis-à-vis d’une requête et exprimant une opinion. Afin de détecter si un document est porteur d’opinion (i.e. comporte de l’information subjective), nous proposons de le comparer à des sources d’information qui comportent du contenu de type opinion. L’intuition derrière cela est la suivante : un document ayant une similarité forte avec des sources d’opinions, est vraisemblablement porteur d’opinion. Pour mesurer cette similarité, nous exploitons des modèles de langue. Nous modélisons le document et la source (référence) porteuse d’opinions par des modèles de langue, nous évaluons ensuite la similarité de ces modèles. Plusieurs expérimentations ont été réalisées sur des collections issues de TREC. Les résultats obtenus valident notre intuition

    Opinion mining: Reviewed from word to document level

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    International audienceOpinion mining is one of the most challenging tasks of the field of information retrieval. Research community has been publishing a number of articles on this topic but a significant increase in interest has been observed during the past decade especially after the launch of several online social networks. In this paper, we provide a very detailed overview of the related work of opinion mining. Following features of our review make it stand unique among the works of similar kind: (1) it presents a very different perspective of the opinion mining field by discussing the work on different granularity levels (like word, sentences, and document levels) which is very unique and much required, (2) discussion of the related work in terms of challenges of the field of opinion mining, (3) document level discussion of the related work gives an overview of opinion mining task in blogosphere, one of most popular online social network, and (4) highlights the importance of online social networks for opinion mining task and other related sub-tasks

    Modèle de langue pour la détection d'opinion dans les blogs

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    Cet article décrit une approche de recherche de documents pertinents vis-à-vis d’une requête et exprimant une opinion. Afin de détecter si un document est porteur d’opinions (i.e; comporte de l’information subjective), nous proposons de le comparer à des sources d’information dont on est sûr qu’elles comportent du contenu de type opinions. L’intuition derrière cela est la suivante, un document ayant une similarité forte avec des sources d’opinions est lui aussi vraisemblablement porteur d’une opinion. Pour mesurer cette similarité nous exploitons des modèles de langues. Nous modélisons le document et la référence porteuse d’opinions par des modèles de langues, nous évaluons ensuite la proximité de ces modèles. Plusieurs expérimentations ont été réalisées sur des collections issues de TREC. Nous proposons de prendre la collection de TREC blog06 comme collection d’analyse et la collection IMDB comme étant la collection de référence

    Hyperlink-extended pseudo relevance feedback for improved microblog retrieval

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    Microblog retrieval has received much attention in recent years due to the wide spread of social microblogging platforms such as Twitter. The main motive behind microblog retrieval is to serve users searching a big collection of microblogs a list of relevant documents (microblogs) matching their search needs. What makes microblog retrieval different from normal web retrieval is the short length of the user queries and the documents that you search in, which leads to a big vocabulary mismatch problem. Many research studies investigated different approaches for microblog retrieval. Query expansion is one of the approaches that showed stable performance for improving microblog retrieval effectiveness. Query expansion is used mainly to overcome the vocabulary mismatch problem between user queries and short relevant documents. In our work, we investigate existing query expansion method (Pseudo Relevance Feedback - PRF) comprehensively, and propose an extension using the information from hyperlinks attached to the top relevant documents. Our experimental results on TREC microblog data showed that Pseudo Relevance Feedback (PRF) alone could outperform many retrieval approaches if configured properly. We showed that combining the expansion terms with the original query by a weight, not to dilute the effect of the original query, could lead to superior results. The weighted combine of the expansion terms is different than what is commonly used in the literature by appending the expansion terms to the original query without weighting. We experimented using different weighting schemes, and empirically found that assigning a small weight for the expansion terms 0.2, and 0.8 for the original query performs the best for the three evaluation sets 2011, 2012, and 2013. We applied the previous weighting scheme to the most reported PRF configuration used in the literature and measured the retrieval performance. The P@30 performance achieved using our weighting scheme was 0.485, 0.4136, and 0.4811 compared to 0.4585, 0.3548, and 0.3861 without applying weighting for the three evaluation sets 2011, 2012 and 2013 respectively. The MAP performance achieved using our weighting scheme was 0.4386, 0.2845, and 0.3262 compared to 0.3592, 0.2074, and 0.2256 without applying weighting for the three evaluation sets 2011, 2012 and 2013 respectively. Results also showed that utilizing hyperlinked documents attached to the top relevant tweets in query expansion improves the results over traditional PRF. By utilizing hyperlinked documents in the query expansion our best runs achieved 0.5000, 0.4339, and 0.5546 P@30 compared to 0.4864, 0.4203, and 0.5322 when applying traditional PRF, and 0.4587, 0.3044, and 0.3584 MAP when applying traditional PRF compared to 0.4405, 0.2850, and 0.3492 when utilizing the hyperlinked document contents (using web page titles, and meta-descriptions) for the three evaluation sets 2011, 2012 and 2013 respectively. We explored different types of information extracted from the hyperlinked documents; we show that using the document titles and meta-descriptions helps in improving the retrieval performance the most. On the other hand, using the meta- keywords degraded the retrieval performance. For the test set released in 2013, using our hyperlinked-extended approach achieved the best improvement over the PRF baseline, 0.5546 P@30 compared to 0.5322 and 0.3584 MAP compared to 0.3492. For the test sets released in 2011 and 2012 we got less improvements over PRF, 0.5000, 0.4339 P@30 compared to 0.4864, 0.4203, and 0.4587, 0.3044 MAP compared to 0.4405, 0.2850. We showed that this behavior was due to the age of the collection, where a lot of hyperlinked documents were taken down or moved and we couldn\u27t get their information. Our best results achieved using hyperlink-extended PRF achieved statistically significant improvements over the traditional PRF for the test sets released in 2011, and 2013 using paired t-test with p-value \u3c 0.05. Moreover, our proposed approach outperformed the best results reported at TREC microblog track for the years 2011, and 2013, which applied more sophisticated algorithms. Our proposed approach achieved 0.5000, 0.5546 P@30 compared to 0.4551, 0.5528 achieved by the best runs in TREC, and 0.4587, 0.3584 MAP compared to 0.3350, 0.3524 for the evaluation sets of 2011 and 2013 respectively. The main contributions of our work can be listed as follows: 1. Providing a comprehensive study for the usage of traditional PRF with microblog retrieval using various configurations. 2. Introducing a hyperlink-based PRF approach for microblog retrieval by utilizing hyperlinks embedded in initially retrieved tweets, which showed a significant improvement to retrieval effectiveness

    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

    Recherche de conversations dans les réseaux sociaux : modélisation et expérimentations sur Twitter

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    National audienceLa problématique étudiée dans cet article est celle de l'indexation et de la recherche de conversations dans les réseaux sociaux. Une conversation est un ensemble de messages échangés entre utilisateurs, à la suite d'un message initial. La démarche proposée se base sur une modélisation probabiliste, et détaille en particulier l'utilisation d'informations sociales dans le réseau Twitter. Notre proposition est évaluée sur un corpus de conversations contenant plus de 50 000 tweets, et sur un ensemble de 15 requêtes tirées pour partie des campagnes TREC Microblog (Lin et Efron, 2013). Les résultats obtenus en combinant les éléments de contenu et les éléments sociaux sur ce corpus sont statistiquement significativement meilleurs que ceux de notre approche utilisant le contenu seul ainsi que ceux d'une approche à base de BM25

    A Time-Aware Approach to Improving Ad-hoc Information Retrieval from Microblogs

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    There is an immense number of short-text documents produced as the result of microblogging. The content produced is growing as the number of microbloggers grows, and as active microbloggers continue to post millions of updates. The range of topics discussed is so vast, that microblogs provide an abundance of useful information. In this work, the problem of retrieving the most relevant information in microblogs is addressed. Interesting temporal patterns were found in the initial analysis of the study. Therefore the focus of the current work is to first exploit a temporal variable in order to see how effectively it can be used to predict the relevance of the tweets and, then, to include it in a retrieval weighting model along with other tweet-specific features. Generalized Linear Mixed-effect Models (GLMMs) are used to analyze the features and to propose two re-ranking models. These two models were developed through an exploratory process on a training set and then were evaluated on a test set

    Representation and learning schemes for sentiment analysis.

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    This thesis identifies four novel techniques of improving the performance of sentiment analysis of text systems. Thes include feature extraction and selection, enrichment of the document representation and exploitation of the ordinal structure of rating classes. The techniques were evaluated on four sentiment-rich corpora, using two well-known classifiers: Support Vector Machines and Na¨ıve Bayes. This thesis proposes the Part-of-Speech Pattern Selector (PPS), which is a novel technique for automatically selecting Part-of-Speech (PoS) patterns. The PPS selects its patterns from a background dataset by use of a number of measures including Document Frequency, Information Gain, and the Chi-Squared Score. Extensive empirical results show that these patterns perform just as well as the manually selected ones. This has important implications in terms of both the cost and the time spent in manual pattern construction. The position of a phrase within a document is shown to have an influence on its sentiment orientation, and that document classification performance can be improved by weighting phrases in this regard. It is, however, also shown to be necessary to sample the distribution of sentiment rich phrases within documents of a given domain prior to adopting a phrase weighting criteria. A key factor in choosing a classifier for an Ordinal Sentiment Classification (OSC) problem is its ability to address ordinal inter-class similarities. Two types of classifiers are investigated: Those that can inherently solve multi-class problems, and those that decompose a multi-class problem into a sequence of binary problems. Empirical results showed the former to be more effective with regard to both mean squared error and classification time performances. Important features in an OSC problem are shown to distribute themselves across similar classes. Most feature selection techniques are ignorant of inter-class similarities and hence easily overlook such features. The Ordinal Smoothing Procedure (OSP), which augments inter-class similarities into the feature selection process, is introduced in this thesis. Empirical results show the OSP to have a positive effect on mean squared error performance

    Using Semantic-Based User Profile Modeling for Context-Aware Personalised Place Recommendations

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    Place Recommendation Systems (PRS's) are used to recommend places to visit to World Wide Web users. Existing PRS's are still limited by several problems, some of which are the problem of recommending similar set of places to different users (Lack of Personalization) and no diversity in the set of recommended items (Content Overspecialization). One of the main objectives in the PRS's or Contextual suggestion systems is to fill the semantic gap among the queries and suggestions and going beyond keywords matching. To address these issues, in this study we attempt to build a personalized context-aware place recommender system using semantic-based user profile modeling to address the limitations of current user profile building techniques and to improve the retrieval performance of personalized place recommender system. This approach consists of building a place ontology based on the Open Directory Project (ODP), a hierarchical ontology scheme for organizing websites. We model a semantic user profile from the place concepts extracted from place ontology and weighted according to their semantic relatedness to user interests. The semantic user profile is then exploited to devise a personalized recommendation by re-ranking process of initial search results for improving retrieval performance. We evaluate this approach on dataset obtained using Google Paces API. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based place recommendation model
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