Analyse de contenu | EPFL Graph Search

L’analyse de contenu est une des méthodologies qualitatives utilisées dans les sciences sociales et humaines. On retrouve par exemple des approches en analyse de contenu en sociologie, en communication, en linguistique, en psychologie. Introduction Une analyse de contenu consiste en un examen systématique et méthodique de documents textuels ou visuels. Dans une analyse de contenu le chercheur tente de minimiser les éventuels biais cognitifs et culturels en s’assurant de l’objectivité de sa recherche. L’analyse de contenu est particulièrement utilisée en sciences sociales et humaines depuis les années 1950. Elle est, à tort, souvent associée à une méthode propre aux sciences sociales nord-américaines. Mais de nombreux théoriciens et chercheurs européens ont contribué au développement des différentes approches d’analyse de contenu. Étapes Sélection analysée Une analyse de contenu nécessite une sélection de documents textuels, ou sonores. Cette sélect

Social Network Analysis for Automatic Role Recognition

Sarah Favre

The computing community has shown a significant interest for the analysis of social interactions in the last decade. Different aspects of social interactions have been studied such as dominance, emotions, conflicts, etc. However, the recognition of roles has been neglected whereas these are a key aspect of social interactions. In fact, sociologists have shown not only that people play roles each time they interact, but also that roles shape behavior and expectations of interacting participants. The aim of this thesis is to fill this gap by investigating the problem of automatic role recognition in a wide range of interaction settings, including production environments, e.g. news and talk-shows, and spontaneous exchanges, e.g. meetings. The proposed role recognition approach includes two main steps. The first step aims at representing the individuals involved in an interaction with feature vectors accounting for their relationships with others. This step includes three main stages, namely segmentation of audio into turns (i.e. time intervals during which only one person talks), conversion of the sequence of turns into a social network, and use of the social network as a tool to extract features for each person. The second step uses machine learning methods to map the feature vectors into roles. The experiments have been carried out over roughly 90 hours of material. This is not only one of the largest databases ever used in literature on role recognition, but also the only one, to the best of our knowledge, including different interaction settings. In the experiments, the accuracy of the percentage of data correctly labeled in terms of roles is roughly 80% in production environments and 70% in spontaneous exchanges (lexical features have been added in the latter case). The importance of roles has been assessed in an application scenario as well. In particular, the thesis shows that roles help to segment talk-shows into stories, i.e. time intervals during which a single topic is discussed, with satisfactory performance. The main contributions of this thesis are as follows: To the best of our knowledge, this is the first work where social network analysis is applied to automatic analysis of conversation recordings. This thesis provides the first quantitative measure of how much roles constrain conversations, and a large corpus of recordings annotated in terms of roles. The results of this work have been published in one journal paper, and in five conference articles.

EPFL2011

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Styliani Asimina Giannakopoulou

Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. Existing solutions that attempt to automate the data cleaning procedure treat data cleaning as a separate offline process that takes place before analysis begins, while also focusing on a specific use-case. In addition, when the analysis involves complex non-relational data such as graphs, data cleaning becomes more challenging as it involves expensive operations. Therefore, offline, specialized cleaning tools exhibit long running times or fail to process large datasets. At the same time, applying data cleaning before analysis starts assumes a priori knowledge of the inconsistencies and the query workload, thereby requiring effort on understanding and cleaning data that is unnecessary for the analysis. Therefore, from a user's perspective, one is forced to use a different, potentially inefficient tool for each category of errors.In this thesis we aim for coverage and efficiency of data cleaning. We design and build data cleaning systems that employ high-level abstractions to (a) represent and optimize different cleaning operations for data of various formats, and (b) allow for real-time data cleaning that is relevant to data analysis.We introduce CleanM, a language that can express multiple types of cleaning operations. CleanM goes through a three-level translation process for optimization purposes; a different family of optimizations is applied in each abstraction level. Thus, CleanM can express complex data cleaning tasks, optimize them in a unified way, and deploy them in a scaleout fashion.To further reduce the data-to-insight time, we propose an approach that performs probabilistic repair of denial constraint violations on-demand, driven by the exploratory analysis that users perform. We introduce Daisy, a system that seamlessly integrates data cleaning into the analysis by relaxing query results. Daisy executes analytical query-workloads over dirty data by weaving cleaning operators into the query plan.To cover complex data such as graphs, we optimize the building block of data cleaning operations on graph data, that is subgraph matching. Subgraph matching constitutes the main bottleneck when cleaning graph data as it is an NP-complete problem. To optimize subgraph matching, we present a scale-up, radix-based algorithm that starts from an arbitrary partitioning of the graph and coordinates parallel pattern matching to eliminate redundant work among the workers. To address load imbalance, we employ a work stealing technique, specific to the subgraph matching problem. Worker threads steal work from straggler threads by using a heuristic that maximizes the work stolen, while at the same time preserves the order of evaluating candidate vertices.Overall, instead of using offline, specialised tools, this thesis designs abstractions that optimize different cleaning primitives over heterogeneous data, while also integrating data cleaning tasks seamlessly into data analysis. Specifically, we provide (a) a declarative high-level interface backed by an optimizable query calculus and (b) the required optimizations at the underlying data cleaning layers while also taking into consideration the exploratory analysis that users perform.

EPFL2021