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.
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
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