Finding and Quantifying Temporal-Aware Contradiction in Reviews

  • Ismail BadacheEmail author
  • Sébastien Fournier
  • Adrian-Gabriel Chifu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10648)


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 The results confirm the effectiveness of our approach to find and quantify contradiction intensity.


Sentiment analysis Aspect detection Contradiction intensity 



The project leading to this publication has received funding from Excellence Initiative of Aix-Marseille University - A*MIDEX, a French “Investissements d’Avenir” programme.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ismail Badache
    • 1
    Email author
  • Sébastien Fournier
    • 1
  • Adrian-Gabriel Chifu
    • 1
  1. 1.LSIS UMR 7296 CNRSUniversity Aix-MarseilleMarseilleFrance

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