Aspect Extraction from Reviews using Convolutional Neural Networks and Embeddings

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

6 Citations (Scopus)
176 Downloads (Pure)


Aspect-based sentiment analysis is an important natural language processing task that allows to extract the sentiment expressed in a review for parts or aspects of a product or service. Extracting all aspects for a domain without manual rules or annotations is a major challenge. In this paper, we propose a method for this task based on a Convolutional Neural Network (CNN) and two embedding layers. We address shortcomings of state-of-the-art methods by combining a CNN with an embedding layer trained on the general domain and one trained the specific domain of the reviews to be analysed. We evaluated our system on two SemEval datasets and compared against state-of-the-art methods that have been evaluated on the same data. The results indicate that our system performs comparably well or better than more complex systems that may take longer to train.
Original languageEnglish
Title of host publicationNatural Language Processing and Information Systems. NLDB 2019.
Number of pages415
ISBN (Electronic)9783030232818
Publication statusPublished - 21 Jun 2019
EventNLDB 2019: Natural Language Processing and Information Systems - University of Salford, Salford, United Kingdom
Duration: 26 Jun 201928 Jun 2019

Publication series

NameLecture Notes in Computer Science


ConferenceNLDB 2019: Natural Language Processing and Information Systems
Abbreviated titleNLDB 2019
Country/TerritoryUnited Kingdom
Internet address


  • Aspect-based sentiment analysis
  • Aspect extraction
  • Convolutional Neural Networks
  • Deep learning
  • NLP


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