Academic Publications

@conference{badache2018contradiction,

title = {Contradiction in Reviews: is it Strong or Low?},

author = {Ismail Badache and Sébastien Fournier and Adrian-Gabriel Chifu},

url = {http://ceur-ws.org/Vol-2078/paper1.pdf},

year  = {2018},

date = {2018-03-01},

urldate = {2018-01-01},

booktitle = {40th European Conference on Information Retrieval, ECIR 2018-BroDyn: Workshop on Analysis of Broad Dynamic Topics over Social Media},

series = {ECIR2018 - BroDyn},

abstract = {Analysis of opinions (reviews) generated by users becomes increasingly exploited by a variety of applications. It allows to follow the evolution of the opinions or to carry out investigations on web resource (e.g. courses, movies, products). The detection of contradictory opinions is an important task to evaluate the latter. This paper focuses on the problem of detecting and estimating contradiction intensity based on the sentiment analysis around specific aspects of a resource. Firstly, certain aspects are identified, according to the distributions of the emotional terms in the vicinity of the most frequent names in the whole of the reviews. Secondly, the polarity of each review segment containing an aspect is estimated using the state-of-the-art approach SentiNeuron. Then, only the resources containing these aspects with opposite polarities (positive, negative) are considered. Thirdly, a measure of the intensity of the contradiction is introduced. It is based on the joint dispersion of the polarity and the rating of the reviews containing the aspects within each resource. The evaluation of the proposed approach is conducted on the Massive Open Online Courses collection containing 2244 courses and their 73,873 reviews, collected from Coursera. The results revealed the effectiveness of the proposed approach to detect and quantify contradictions.},

keywords = {Aspect Detection, Contradiction Intensity, Sentiment Analysis},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {Les réactions des stakeholders aux allégations d'irresponsabilité organisationnelle : le cas du scandale Volkswagen},

author = {Amélie Bohas and Marie-Aude Abid-Dupont and Tarek Abid and Adrian Chifu and Sébastien Fournier},

year  = {2017},

date = {2017-10-01},

urldate = {2017-10-01},

booktitle = {12ème congrès du RIODD},

series = {RIODD2017},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{badache2017harnessing,

title = {Harnessing Ratings and Aspect-Sentiment to Estimate Contradiction Intensity in Temporal-Related Reviews},

author = {Ismail Badache and Sébastien Fournier and Adrian-Gabriel Chifu},

url = {https://reader.elsevier.com/reader/sd/pii/S1877050917315922?token=C2C0B55134B8BC01252FFB23B143E73FD75574DECFBBA3B2D8F2DE5A7CA9F0C6432D5E16D7899EC273C6176F76138D50},

year  = {2017},

date = {2017-09-01},

urldate = {2017-09-01},

booktitle = {21th International Conference on Knowledge Based and Intelligent Information and Engineering Systems},

journal = {Procedia computer science},

volume = {112},

pages = {1711--1720},

publisher = {Elsevier},

series = {KES2017},

abstract = {Analysis of opinions (reviews) generated by users becomes increasingly exploited by a variety of applications. It allows to follow the evolution of the opinions or to carry out investigations on products. The detection of contradictory opinions about a web resource (e.g., courses, movies, products, etc.) is an important task to evaluate the latter. This paper focuses on the problem of detecting contradictions in reviews based on the sentiment analysis around specific aspects of a resource (document). In general, for web resources such as online courses (e.g. on Coursera or edX), reviews are often generated during course sessions. Between each session users stop reviewing on the course, and this course may have updates. So, in order to avoid the confusion of contradictory reviews coming from two or more different sessions, the reviews related to a given resource should be firstly grouped according to their session. Secondly, certain aspects are extracted according to the distributions of the emotional terms in the vicinity of the most frequent names in the reviews collection. Thirdly, the polarity of each review segment containing an aspect is identified. Then taking only the resources containing these aspects with opposite polarities (positive, negative). Finally, we propose a measure of contradiction intensity based on the joint dispersion of the polarity and the rating of the reviews containing the aspects within each resource. The evaluation of our approach is conducted on the Massive Open Online Courses (MOOC) collection containing 2244 courses and their 73,873 reviews, collected from Coursera. The results of experiments revealed the effectiveness of the proposed approach to capture and quantify contradiction intensity},

keywords = {Aspect Extraction, Contradiction Intensity, Rating, Review, Sentiment Analysis, Time},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chifu2017,

title = {Role of social media in propagating controversies: the case of cultural microblog feeds},

author = {Adrian Chifu and Fidelia Ibekwe-Sanjuan and Nathanaêla Andrianasolo},

url = {http://ceur-ws.org/Vol-1866/paper_129.pdf},

year  = {2017},

date = {2017-09-01},

urldate = {2017-09-01},

booktitle = {The 8th Conference and Labs of the Evaluation Forum - CLEF Microblog Cultural Contextualization},

series = {CLEF2017},

abstract = {The aim of this research is to investigate how social media mediate social controversies in the public arena. For that, we will use the CLEF MC2 corpus of microblogs that captured long term political and cultural controversies in order to follow the birth and development of controversies across time and pinpoint the increasing role that social media play in their propagation, regulation and resolution.},

keywords = {Focus IR, Information Vizualization, Opinion Mining},

pubstate = {published},

tppubtype = {conference}

}

@conference{Badache2017,

title = {Détection de contradiction dans les commentaires},

author = {Ismail Badache and Sébastien Fournier and Adrian Chifu},

url = {http://www.asso-aria.org/coria/2017/17.pdf},

year  = {2017},

date = {2017-03-29},

booktitle = {COnférence en Recherche d'Information et Applications},

series = {CORIA2017},

abstract = {L’analyse des avis (commentaires) générés par les utilisateurs devient de plus en plus exploitable par une variété d’applications. Elle permet de suivre l’évolution des avis ou d’effec- tuer des enquêtes sur des produits. La détection d’avis contradictoires autour d’une ressource Web (ex. cours, film, produit, etc.) est une tâche importante pour évaluer cette dernière. Dans cet article, nous nous concentrons sur le problème de détection des contradictions et de la me- sure de leur intensité en se basant sur l’analyse du sentiment autour des aspects spécifiques à une ressource (document). Premièrement, nous identifions certains aspects, selon les distri- butions des termes émotionnels au voisinage des noms les plus fréquents dans l’ensemble des commentaires. Deuxièmement, nous estimons la polarité de chaque segment de commentaire contenant un aspect. Ensuite, nous prenons uniquement les ressources contenant ces aspects avec des polarités opposées (positive, négative). Troisièmement, nous introduisons une mesure de l’intensité de la contradiction basée sur la dispersion conjointe de la polarité et du rating des commentaires contenant les aspects au sein de chaque ressource. Nous évaluons l’effica- cité de notre approche sur une collection de MOOC (Massive Open Online Courses) contenant 2244 cours et leurs 73873 commentaires, collectés à partir de Coursera. Nos résultats montrent l’efficacité de l’approche proposée pour capturer les contradictions de manière significative.},

keywords = {Analyse de sentiments, Contenus générés par l'utilisateur, Contradiction},

pubstate = {published},

tppubtype = {conference}

}

@conference{badache2017finding,

title = {Finding and Quantifying Temporal-Aware Contradiction in Reviews},

author = {Ismail Badache and Sébastien Fournier and Adrian-Gabriel Chifu},

url = {https://hal.archives-ouvertes.fr/hal-01904434/file/2017_AIRS_BADACHE.pdf},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {Asia Information Retrieval Symposium},

pages = {167--180},

organization = {Springer},

series = {AIRS2017},

abstract = {Opinions (reviews) on web resources (e.g., courses, movies), generated by users, become increasingly exploited in text analysis tasks, the detection of contradictory opinions being one of them. This paper focuses on the quantification of sentiment-based contradictions around specific aspects in reviews. However, it is necessary to study the contradictions with respect to the temporal dimension of reviews (their sessions). In general, for web resources such as online courses (e.g. coursera or edX), reviews are often generated during the course sessions. Between sessions, users stop reviewing courses, and there are chances that courses will be updated. So, in order to avoid the confusion of contradictory reviews coming from two or more different sessions, the reviews related to a given resource should be firstly grouped according to their corresponding session. Secondly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Thirdly, the polarity of each review segment containing an aspect is estimated. Then, only resources containing these aspects with opposite polarities are considered. Finally, the contradiction intensity is estimated based on the joint dispersion of polarities and ratings of the reviews containing aspects. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. The results confirm the effectiveness of our approach to find and quantify contradiction intensity.},

keywords = {Aspect Detection, Contradiction Intensity, Sentiment Analysis},

pubstate = {published},

tppubtype = {conference}

}

@conference{chifu2017human,

title = {Human-Based Query Difficulty Prediction},

author = {Adrian-Gabriel Chifu and Sébastien Déjean and Stefano Mizzaro and Josiane Mothe},

url = {https://hal.archives-ouvertes.fr/hal-01712541/document},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {European Conference on Information Retrieval},

pages = {343--356},

organization = {Springer},

series = {ECIR2017},

abstract = {The purpose of an automatic query difficulty predictor is to decide whether an information retrieval system is able to provide the most appropriate answer for a current query. Researchers have investigated many types of automatic query difficulty predictors. These are mostly related to how search engines process queries and documents: they are based on the inner workings of searching/ranking system functions, and therefore they do not provide any really insightful explanation as to the reasons for the difficulty, and they neglect user-oriented aspects. In this paper we study if humans can provide useful explanations, or reasons, of why they think a query will be easy or difficult for a search engine. We run two experiments with variations in the TREC reference collection, the amount of information available about the query, and the method of annotation generation. We examine the correlation between the human prediction, the reasons they provide, the automatic prediction, and the actual system effectiveness. The main findings of this study are twofold. First, we confirm the result of previous studies stating that human predictions correlate only weakly with system effectiveness. Second, and probably more important, after analyzing the reasons given by the annotators we find that: (i) overall, the reasons seem coherent, sensible, and informative; (ii) humans have an accurate picture of some query or term characteristics; and (iii) yet, they cannot reliably predict system/query difficulty.},

keywords = {Free Text, Free Text Comment, Human Annotator, Query Suggestion, Query Term},

pubstate = {published},

tppubtype = {conference}

}