MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews

  • Erfan Ghadery University of Tehran
  • Sajad Movahedi University of Tehran
  • Heshaam Faili University of Tehran
  • Azadeh Shakery University of Tehran


The advent of the Internet has caused a significant growth in the number of opinions expressed about products or services on e-commerce websites. Aspect category detection, which is one of the challenging subtasks of aspect-based sentiment analysis, deals with categorizing a given review sentence into a set of predefined categories. Most of the research efforts in this field are devoted to English language reviews, while there are a large number of reviews in other languages that are left unexplored. In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings. To the best of our knowledge, our method is the first attempt at performing aspect category detection on multiple languages simultaneously. Empirical results on the multilingual dataset provided by SemEval workshop demonstrate the effectiveness of the proposed method1.

AAAI Technical Track: Natural Language Processing