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Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

  • Soufian JebbaraEmail author
  • Philipp Cimiano
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 641)

Abstract

The World Wide Web holds a wealth of information in the form of unstructured texts such as customer reviews for products, events and more. By extracting and analyzing the expressed opinions in customer reviews in a fine-grained way, valuable opportunities and insights for customers and businesses can be gained.

We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic Sentiment Analysis. Our proposed architecture divides the task in two subtasks: aspect term extraction and aspect-specific sentiment extraction. This approach is flexible in that it allows to address each subtask independently. As a first step, a recurrent neural network is used to extract aspects from a text by framing the problem as a sequence labeling task. In a second step, a recurrent network processes each extracted aspect with respect to its context and predicts a sentiment label. The system uses pretrained semantic word embedding features which we experimentally enhance with semantic knowledge extracted from WordNet. Further features extracted from SenticNet prove to be beneficial for the extraction of sentiment labels. As the best performing system in its category, our proposed system proves to be an effective approach for Aspect-Based Sentiment Analysis.

Notes

Acknowledgements

This work was supported by the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Semantic Computing Group, Cognitive Interaction Technology – Center of Excellence (CITEC)Bielefeld UniversityBielefeldGermany

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