2022
Journal Articles
Ismail Badache; Adrian-Gabriel Chifu; Sébastien Fournier
Unsupervised and Supervised Methods to Estimate Temporal-Aware Contradictions in Online Course Reviews Journal Article
In: Mathematics, vol. 10, no. 5, 2022.
Abstract | Links | BibTeX | Tags: Aspect Detection, Contradiction Intensity, Feature Evaluation, Rating, Sentiment Analysis, Temporality
@article{badache2022,
title = {Unsupervised and Supervised Methods to Estimate Temporal-Aware Contradictions in Online Course Reviews},
author = {Ismail Badache and Adrian-Gabriel Chifu and Sébastien Fournier},
editor = {MDPI},
url = {https://www.mdpi.com/2227-7390/10/5/809},
doi = {10.3390/math10050809},
year = {2022},
date = {2022-03-03},
urldate = {2022-03-03},
journal = {Mathematics},
volume = {10},
number = {5},
abstract = {The analysis of user-generated content on the Internet has become increasingly popular for a wide variety of applications. One particular type of content is represented by the user reviews for programs, multimedia, products, and so on. Investigating the opinion contained by reviews may help in following the evolution of the reviewed items and thus in improving their quality. Detecting contradictory opinions in reviews is crucial when evaluating the quality of the respective resource. This article aims to estimate the contradiction intensity (strength) in the context of online courses (MOOC). This estimation was based on review ratings and on sentiment polarity in the comments, with respect to specific aspects, such as “lecturer”, “presentation”, etc. Between course sessions, users stop reviewing, and also, the course contents may evolve. Thus, the reviews are time dependent, and this is why they should be considered grouped by the course sessions. Having this in mind, the contribution of this paper is threefold: (a) defining the notion of subjective contradiction around specific aspects and then estimating its intensity based on sentiment polarity, review ratings, and temporality; (b) developing a dataset to evaluate the contradiction intensity measure, which was annotated based on a user study; (c) comparing our unsupervised method with supervised methods with automatic feature selection, over the dataset. The dataset collected from coursera.org is in English. It includes 2244 courses and 73,873 user-generated reviews of those courses.The results proved that the standard deviation of the ratings, the standard deviation of the polarities, and the number of reviews are suitable features for predicting the contradiction intensity classes. Among the supervised methods, the J48 decision trees algorithm yielded the best performance, compared to the naive Bayes model and the SVM model.},
keywords = {Aspect Detection, Contradiction Intensity, Feature Evaluation, Rating, Sentiment Analysis, Temporality},
pubstate = {published},
tppubtype = {article}
}
The analysis of user-generated content on the Internet has become increasingly popular for a wide variety of applications. One particular type of content is represented by the user reviews for programs, multimedia, products, and so on. Investigating the opinion contained by reviews may help in following the evolution of the reviewed items and thus in improving their quality. Detecting contradictory opinions in reviews is crucial when evaluating the quality of the respective resource. This article aims to estimate the contradiction intensity (strength) in the context of online courses (MOOC). This estimation was based on review ratings and on sentiment polarity in the comments, with respect to specific aspects, such as “lecturer”, “presentation”, etc. Between course sessions, users stop reviewing, and also, the course contents may evolve. Thus, the reviews are time dependent, and this is why they should be considered grouped by the course sessions. Having this in mind, the contribution of this paper is threefold: (a) defining the notion of subjective contradiction around specific aspects and then estimating its intensity based on sentiment polarity, review ratings, and temporality; (b) developing a dataset to evaluate the contradiction intensity measure, which was annotated based on a user study; (c) comparing our unsupervised method with supervised methods with automatic feature selection, over the dataset. The dataset collected from coursera.org is in English. It includes 2244 courses and 73,873 user-generated reviews of those courses.The results proved that the standard deviation of the ratings, the standard deviation of the polarities, and the number of reviews are suitable features for predicting the contradiction intensity classes. Among the supervised methods, the J48 decision trees algorithm yielded the best performance, compared to the naive Bayes model and the SVM model.
2019
Journal Articles
Ismaïl Badache; Sébastien Fournier; Adrian Chifu
Prédire l'intensité de contradiction dans les commentaires : faible, forte ou très forte ? Journal Article
In: Le Bulletin de l'Association Française pour l'Intelligence Artificielle (AFIA 2019), 2019.
Abstract | Links | BibTeX | Tags: Aspect Detection, Contradiction Intensity, Criteria Evaluation, Sentiment Analysis
@article{Badache2019AFIA,
title = {Prédire l'intensité de contradiction dans les commentaires : faible, forte ou très forte ?},
author = {Ismaïl Badache and Sébastien Fournier and Adrian Chifu},
url = {https://hal.archives-ouvertes.fr/hal-01872267/document},
year = {2019},
date = {2019-12-01},
journal = {Le Bulletin de l'Association Française pour l'Intelligence Artificielle (AFIA 2019)},
abstract = {Reviews on web resources (e.g. courses, movies) become increasingly exploited in text analysis tasks (e.g. opinion detection, controversy detection). This paper investigates contradiction intensity in reviews exploiting different features such as variation of ratings and variation of polarities around specific entities (e.g. aspects, topics). Firstly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Secondly, the polarity of each review segment containing an aspect is estimated. Only resources containing these aspects with opposite polarities are considered. Finally, some features are evaluated, using feature selection algorithms, to determine their impact on the effectiveness of contradiction intensity detection. The selected features are used to learn some state-of-the-art learning approaches. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. Results showed that variation of ratings, variation of polarities, and reviews quantity are the best predictors of contradiction intensity. Also, J48 was the most effective learning approach for this type of classification.},
keywords = {Aspect Detection, Contradiction Intensity, Criteria Evaluation, Sentiment Analysis},
pubstate = {published},
tppubtype = {article}
}
Reviews on web resources (e.g. courses, movies) become increasingly exploited in text analysis tasks (e.g. opinion detection, controversy detection). This paper investigates contradiction intensity in reviews exploiting different features such as variation of ratings and variation of polarities around specific entities (e.g. aspects, topics). Firstly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Secondly, the polarity of each review segment containing an aspect is estimated. Only resources containing these aspects with opposite polarities are considered. Finally, some features are evaluated, using feature selection algorithms, to determine their impact on the effectiveness of contradiction intensity detection. The selected features are used to learn some state-of-the-art learning approaches. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. Results showed that variation of ratings, variation of polarities, and reviews quantity are the best predictors of contradiction intensity. Also, J48 was the most effective learning approach for this type of classification.
2018
Conferences
Ismail Badache; Sébastien Fournier; Adrian-Gabriel Chifu
Predicting Contradiction Intensity: Low, Strong or Very Strong? Conference
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR2018 2018.
Abstract | Links | BibTeX | Tags: Aspect, Contradiction Intensity, Feature Evaluation, Sentiment
@conference{badache2018predicting,
title = {Predicting Contradiction Intensity: Low, Strong or Very Strong?},
author = {Ismail Badache and Sébastien Fournier and Adrian-Gabriel Chifu},
url = {https://hal.archives-ouvertes.fr/hal-01796060/document},
year = {2018},
date = {2018-07-01},
urldate = {2018-01-01},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {1125--1128},
series = {SIGIR2018},
abstract = {Reviews on web resources (e.g. courses, movies) become increasingly exploited in text analysis tasks (e.g. opinion detection, controversy detection). This paper investigates contradiction intensity in reviews exploiting different features such as variation of ratings and variation of polarities around specific entities (e.g. aspects, topics). Firstly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Secondly, the polarity of each review segment containing an aspect is estimated. Only resources containing these aspects with opposite polarities are considered. Finally, some features are evaluated, using feature selection algorithms, to determine their impact on the effectiveness of contradiction intensity detection. The selected features are used to learn some state-of-the-art learning approaches. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. Results showed that variation of ratings, variation of polarities, and reviews quantity are the best predictors of contradiction intensity. Also, J48 was the most effective learning approach for this type of classification.https://hal.archives-ouvertes.fr/hal-01796060/document},
keywords = {Aspect, Contradiction Intensity, Feature Evaluation, Sentiment},
pubstate = {published},
tppubtype = {conference}
}
Reviews on web resources (e.g. courses, movies) become increasingly exploited in text analysis tasks (e.g. opinion detection, controversy detection). This paper investigates contradiction intensity in reviews exploiting different features such as variation of ratings and variation of polarities around specific entities (e.g. aspects, topics). Firstly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Secondly, the polarity of each review segment containing an aspect is estimated. Only resources containing these aspects with opposite polarities are considered. Finally, some features are evaluated, using feature selection algorithms, to determine their impact on the effectiveness of contradiction intensity detection. The selected features are used to learn some state-of-the-art learning approaches. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. Results showed that variation of ratings, variation of polarities, and reviews quantity are the best predictors of contradiction intensity. Also, J48 was the most effective learning approach for this type of classification.https://hal.archives-ouvertes.fr/hal-01796060/document
Ismail Badache; Sébastien Fournier; Adrian-Gabriel Chifu
Contradiction in Reviews: is it Strong or Low? Conference
40th European Conference on Information Retrieval, ECIR 2018-BroDyn: Workshop on Analysis of Broad Dynamic Topics over Social Media, ECIR2018 - BroDyn 2018.
Abstract | Links | BibTeX | Tags: Aspect Detection, Contradiction Intensity, Sentiment Analysis
@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}
}
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.
2017
Conferences
Ismail Badache; Sébastien Fournier; Adrian-Gabriel Chifu
21th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, vol. 112, KES2017 Elsevier, 2017.
Abstract | Links | BibTeX | Tags: Aspect Extraction, Contradiction Intensity, Rating, Review, Sentiment Analysis, Time
@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}
}
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
Ismail Badache; Sébastien Fournier; Adrian-Gabriel Chifu
Finding and Quantifying Temporal-Aware Contradiction in Reviews Conference
Asia Information Retrieval Symposium, AIRS2017 Springer 2017.
Abstract | Links | BibTeX | Tags: Aspect Detection, Contradiction Intensity, Sentiment Analysis
@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}
}
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.
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