Chaîne de traitement pour une approche discursive de l'analyse d'opinion

La structure discursive d'un texte est un élément essentiel à la compréhension du contenu véhiculé par ce texte. Elle affecte, par exemple, la structure temporelle du texte, ou encore l'interprétation des expressions anaphoriques. Dans cette thèse, nous aborderons les effets de la structure discursive sur l'analyse de sentiments. L'analyse des sentiments est un domaine de recherche extrêmement actif en traitement automatique des langues. Devant l'abondance de données subjectives disponibles, l'automatisation de la synthèse des multiples avis devient cruciale pour obtenir efficacement une vue d'ensemble des opinions sur un sujet donné. La plupart des travaux actuels proposent une analyse des opinions au niveau du document ou au niveau de la phrase en ignorant la structure discursive. Dans cette thèse, nous nous plaçons dans le contexte de la théorie de la SDRT (Segmented Discourse Representation Theory) et proposons de répondre aux questions suivantes : -Existe-t-il un lien entre la structure discursive d'un document et les opinions émises dans ce même document ? -Quel est le rôle des relations de discours dans la détermination du caractère objectif ou subjectif d'un segment textuel ? -Quel est le rôle des éléments linguistiques, comme la négation et la modalité, lors de la détermination de la polarité d'un segment textuel subjectif ? -Quel est l'impact de la structure discursive lors de la détermination de l'opinion globale véhiculée dans un document ? -Est-ce qu'une approche basée sur le discours apporte une réelle valeur ajoutée comparée à une approche classique basée sur la notion de 'sacs de mots'? -Cette valeur ajoutée est-elle dépendante du genre de corpus ?The discourse structure of a document is a key element to understand the content conveyed by a text. It affects, for instance, the temporal structure of a text, or the interpretation of anaphoric expressions. The discourse structure showed its usefulness in numerous NLP applications, such as automatic summary, or textual entailment. In this thesis, we will study the effects of the discourse structure on sentiment analysis. Sentiment analysis is an extremely active research domain in natural language processing. The last years have seen the multiplication of the available textual data conveying opinion on the web, and the automation of the summary of opinion documents became crucial for who wants to keep an overview of the opinion on a given subject. A huge interest lies in these data, both for the companies who want to retrieve consumer opinion, and for the consumers willing to gather information. Most of the current research efforts describe an opinion extraction at the document level or at the sentence level, ignoring the discourse structure. In this thesis work, we address opinion extraction through the discourse framework of the SDRT (Segmented Discourse Representation Theory), and try to answer to the following questions: -Is there a link between the discourse structure of a document and the opinions contained in that document? -What is the role of discourse relations in the determination of whether a textual segment is objective or subjective? -What is the impact of the discourse structure in the determination of the overall opinion conveyed by a document? -Does a discourse based approach really bring additional value compared to a classical "bag of words" approach

Measuring the Effect of Discourse Structure on Sentiment Analysis

International audienceThe aim of this paper is twofold: measuring the effect of discourse structure when assessing the overall opinion of a document and analyzing to what extent these effects depend on the corpus genre. Using Segmented Discourse Representation Theory as our formal framework, we propose several strategies to compute the overall rating. Our results show that discourse-based strategies lead to better scores in terms of accuracy and Pearson’s correlation than state-of-the-art approaches

Sentiment Composition Using a Parabolic Model

International audienceIn this paper, we propose a computational model that accounts for the effects of negation and modality on opinion expressions. Based on linguistic experiments informed by native speakers, we distil these effects according to the type of modality and negation. The model relies on a parabolic representation where an opinion expression is represented as a point on a parabola. Negation is modelled as functions over this parabola whereas modality through a family of parabolas of different slopes; each slope corresponds to a different certainty degree. The model is evaluated using two experiments, one involving direct strength judgements on a 7-point scale and the other relying on a sentiment annotated corpus. The empirical evaluation of our model shows that it matches the way humans handle negation and modality in opinionated sentence

Evaluation in Discourse: a Corpus-Based Study

This paper describes the CASOAR corpus, the first manually annotated corpus that explores the impact of discourse structure on sentiment analysis with a study of movie reviews in French and in English as well as letters to the editor in French. While annotating opinions at the expression, the sentence or the document level is a well-established task and relatively straightforward, discourse annotation remains difficult, especially for non-experts. Therefore, combining both annotations poses several methodological problems that we address here. We propose a multi-layered annotation scheme that includes: the complete discourse structure according to the Segmented Discourse Representation Theory, the opinion orientation of elementary discourse units and opinion expressions, and their associated features. We detail each layer, explore the interactions between them and discuss our results. In particular, we examine the correlation between discourse and semantic category of opinion expressions, the impact of discourse relations on both subjectivity and polarity analysis and the impact of discourse on the determination of the overall opinion of a document. Our results demonstrate that discourse is an important cue for sentiment analysis, at least for the corpus genres we have studied

Association between Backward Walking and Cognition in Parkinson Disease: A Systematic Review

Backward walking often occurs in everyday life. It is more complex than forward walking and is associated with decreased coordination. However, it is unclear if a reduced backward walking performance is associated with impaired cognition. This could be particularly relevant as gait and cognitive deficits commonly occur in Parkinson's disease. The objective of this systematic review was to synthesize the evidence on the association between backward walking and cognition in persons with Parkinson's disease. The electronic databases PubMed and Web of Science were systematically searched, and the quality of eligible studies was assessed. Two studies met the inclusion criteria, but study protocols, investigated population, and outcome measures differed substantially. One study showed lower backward walking speed in patients with Parkinson's disease with poorer attention test performances. The second study showed a weak correlation between executive cognitive functions and backward walking speed. Given the low number of studies, the heterogenous study design, and the inconsistent results, the present review highlights the need to further investigate the association between backward walking and cognition in patients with Parkinson's disease

Discourse Segmentation of Opinion Texts

[contribution to the panel Nonveridicality, evaluation and coherence relations, organized by Taboada Maite]International audienceThe extraction of elementary opinion expressions from texts has been much studied during the last decade. Actually, there are relatively effective algorithms for extracting and summarising opinions ((Hatzivassiloglou and McKeown, 1997)(Vernier et al., 2009)). However, as (Polanyi and Zaenen, 2006) stated, identifying prior polarity alone may not suffice to improve sentiment analysis at a finer-grain. Indeed, discourse structure can influence the interpretation of an evaluation both at the clause level, where opinions can be disambiguated, and at the document level, where rhetorical relations can be used to improve the recognition of the overall stance. A computational approach to the discourse analysis of opinion expressions has been recently explored in (Somasundaran, 2009). This work uses two sets of discourse-level relations: relations between targets of opinions, and between opinions themselves. However, this work does not refer to any well-known discourse theory and does not study how discourse segments conveying opinion expressions interact with non opinion discourse segments. The work described in this paper follows (Asher et al., 2009) where Segmented Discourse Representation Theory (SDRT, Asher and Lascarides, 2003) is used in order to get a deeper understanding of contextual polarity. Our aim is to study what are the impact of some discourse relations to compute the strength of opinions, more precisely - how the degree of commitment and the degree of veracity that underline the attribution relation act on an opinion, as in: “Peter affirmed that the owner of the restaurant has changed last year, and thus the quality of the food has significantly dropped.” - how can we analyse opinions that are in the scope of a hypothesis or a condition. - how contrasts influence opinion polarity within a clause. - how a discourse segment conveying an implicit opinion can be disambiguated, as in : “This restaurant is really nice, but has no terrace.” - how the discourse graph can be used to compute the overall opinion To reach this goal, automatically detecting discourse segments is an important preliminary step. We propose an approach to opinion discourse segmentation that allows for identifying elementary opinion units. This enables us to define a projection of the relations from the discourse segments level to the opinion level. Moreover, the granularity of the segmentation allows the detection of relations within syntactic constituents like verb phrases, such as the contrast in “This restaurant is good but expensive.” Our segmenter is developed on the top of the discourse segmenter (Afantenos et al, 2010) of the French ANNODISproject (an ongoing effort to create a discourse bank for French). We use a set of rules that refine the ANNODIS segments using syntactic and semantic features, such as coordination words or contrast marquers

L’apprentissage d’ordonnancement pour l’appariement de questions

National audienceCet article présente une approche permettant à un utilisateur d’interroger une base de connaissances type FAQ, c’est-à-dire un ensemble de questions et leurs réponses respectives rédigées en langue naturelle. Le composant présenté dans cet article apparie la question de l’utilisateur à une ou plusieurs questions de la base de connaissances. Pour cela, nous utilisons un composant déjà existant d’analyse de questions, capable de sélectionner un ensemble de questions candidates proches de la question utilisateur, et de produire des traits propres à chaque couple (question utilisateur, question candidate). Ce composant est chaîné à un modèle permettant l’ordonnancement des questions candidates, qui est appris automatiquement de façon supervisée, une partie seulement du corpus d'apprentissage étant annotée manuellement, et le reste grâce à des règles add-hoc. Ces travaux reprennent les résultats d’un domaine de recherche récent, l’apprentissage d’ordonnancement (Learning to Rank), et les adaptent à une application industrielle innovante, l’appariement de questions comme paradigme d’accès à la connaissance. Une expérimentation évalue sur des données issues d’un système en production la qualité de chacune des phases d’apprentissage