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

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

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

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

Methods for ranking user-generated text streams: a case study in blog feed retrieval

User generated content are one of the main sources of information on the Web nowadays. With the huge amount of this type of data being generated everyday, having an efficient and effective retrieval system is essential. The goal of such a retrieval system is to enable users to search through this data and retrieve documents relevant to their information needs. Among the different retrieval tasks of user generated content, retrieving and ranking streams is one of the important ones that has various applications. The goal of this task is to rank streams, as collections of documents with chronological order, in response to a user query. This is different than traditional retrieval tasks where the goal is to rank single documents and temporal properties are less important in the ranking. In this thesis we investigate the problem of ranking user-generated streams with a case study in blog feed retrieval. Blogs, like all other user generated streams, have specific properties and require new considerations in the retrieval methods. Blog feed retrieval can be defined as retrieving blogs with a recurrent interest in the topic of the given query. We define three different properties of blog feed retrieval each of which introduces new challenges in the ranking task. These properties include: 1) term mismatch in blog retrieval, 2) evolution of topics in blogs and 3) diversity of blog posts. For each of these properties, we investigate its corresponding challenges and propose solutions to overcome those challenges. We further analyze the effect of our solutions on the performance of a retrieval system. We show that taking the new properties into account for developing the retrieval system can help us to improve state of the art retrieval methods. In all the proposed methods, we specifically pay attention to temporal properties that we believe are important information in any type of streams. We show that when combined with content-based information, temporal information can be useful in different situations. Although we apply our methods to blog feed retrieval, they are mostly general methods that are applicable to similar stream ranking problems like ranking experts or ranking twitter users

The Use Of Kullback-Leibler Divergence In Opinion Retrieval

With the huge amount of subjective contents in on-line documents, there is a clear need for an information retrieval system that supports retrieval of documents containing opinions about the topic expressed in a user’s query. In recent years, blogs, a new publishing medium, have attracted a large number of people to express personal opinions covering all kinds of topics in response to the real-world events. The opinionated nature of blogs makes them a new interesting research area for opinion retrieval. Identification and extraction of subjective contents from blogs has become the subject of several research projects. In this thesis, four novel methods are proposed to retrieve blog posts that express opinions about the given topics. The first method utilizes the Kullback-Leibler divergence (KLD) to weight the lexicon of subjective adjectives around query terms. Considering the distances between the query terms and subjective adjectives, the second method uses KLD scores of subjective adjectives based on distances from the query terms for document re-ranking. The third method calculates KLD scores of subjective adjectives for predefined query categories. In the fourth method, collocates, words co-occurring with query terms in the corpus, are used to construct the subjective lexicon automatically. The KLD scores of collocates are then calculated and used for document ranking. Four groups of experiments are conducted to evaluate the proposed methods on the TREC test collections. The results of the experiments are compared with the baseline systems to determine the effectiveness of using KLD in opinion retrieval. Further studies are recommended to explore more sophisticated approaches to identify subjectivity and promising techniques to extract opinions

Retrieving opinions from discussion forums

Understanding the landscape of opinions on a given topic or issue is important for policy makers, sociologists, and intelligence analysts. The first step in this process is to retrieve relevant opinions. Discussion forums are potentially a good source of this information, but comes with a unique set of retrieval challenges. In this short paper, we test a range of existing techniques for forum retrieval and develop new retrieval models to differentiate between opinionated and factual forum posts. We are able to demonstrate some significant performance improvements over the baseline retrieval models, demonstrating that this as a promising avenue for further study. Copyright is held by the owner/author(s).EI

Exploiting multiple sources of evidence for opinion search in the web

In this thesis we study Opinion Mining and Sentiment Analysis and propose a ne-grained analysis of the opinions conveyed in texts. Concretely, the aim of this research is to gain an understanding on how to combine di erent types of evidence to e ectively determine on-topic opinions in texts. To meet this aim, we consider content-match evidence, obtained at document and passage level, as well as di erent structural aspects of the text. Current Opinion Mining technology is not mature yet. As a matter of fact, people often use regular search engines, which lack evolved opinion search ca- pabilities, to nd opinions about their interests. This means that the e ort of detecting what are the key relevant opinions relies on the user. The lack of widely accepted Opinion Mining technology is due to the limitations of cur- rent models, which are simplistic and perform poorly. In this thesis we study a speci c set of factors that are indicative of subjectivity and relevance and we try to understand how to e ectively combine them to detect opinionated docu- ments, to extract relevant opinions and to estimate their polarity. We propose innovative methods and models able to incorporate di erent types of evidence and it is our intention to contribute in di erent areas, including those related to i) search for opinionated documents, ii) detection of subjectivity at docu- ment and passage level, and iii) estimation of polarity. An important concern that guides this research is e ciency. Some types of evidence, such as discourse structure, have only been tested with small collections from narrow domains (e.g., movie reviews). We demonstrate here that evolved linguistic features { based on discourse analysis{ can potentially lead to a better understanding of how subjectivity ows in texts. And we show that this type of features can be e ciently injected into general-purpose opinion retrieval solutions that operate at large scale

Integrating Non-Topical Aspects Into Information Retrieval

When users investigate a topic, they are often interested in results that are not just relevant, but also strongly opinionated or covering a range of times. To get such results, users are forced to formulate ambiguous, complex, or longer queries. Commonly this becomes a burden, since users need to issue several queries with reformulations if initial search results are not completely satisfactory. In this thesis, we focus on those two non-topical dimensions: opinionatedness and time. We develop measures for quantifying them in documents and incorporate them into search results. For improving search results with respect to non-topical dimensions, we use diversification approaches. To achieve controlled variety in results, our methods are integrated with a general bias framework, which seamlessly unifies extreme biases for each dimension. Results can be diversified across a single or multiple non-topical dimensions. Our experiments are performed on the TREC Blog Track. As a result of this research, we can determine how temporal or opinionated a unit of text is. By means of diversification we provide a retrieval framework to users with which they can more easily find different kinds of opinionated or temporal results with only one submitted query. The burden of analyzing pre-existing biases for a query and discovering times at which important events happened is fully carried by the system. As opposed to prior work in this area, pre-existing biases in search results are analyzed, and diversification is performed in a controlled manner for each dimension. We show how to combine several dimensions with individual biases for each, while also presenting approaches to time and sentiment diversification. The insights from this work will be very valuable for next generation search engines and retrieval systems

Looking at things differently: Exploring perspective recall for informal text retrieval

When retrieving informal text such as blogs, comments, contributions to discussion forums, users often want to uncover different perspectives on a given issue. To help uncover perspectives, we examine the use of query expansion against multiple external corpora. We consider two informal text retrieval tasks: blog post finding and blog finding. We operationalize the idea of uncovering multiple perspectives by query expansion against multiple corpora from different genres. We use two approaches to incorporate these perspectives: as a rank-based combination of runs and a mixture of query models. The use of external sources does indeed generate different views on a topic as becomes clear from the unique relevant results identified by the expanded runs compared to the baseline run. Even after combining the expanded run with the original run, unique relevant documents are found by both of the perspectives. As to the combination methods, the mixture of query models outperforms the rank combination, and leads to significant improvements in MAP score over the baseline