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

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Semantic Web Challenges (SemWebEval 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 641))

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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.

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Notes

  1. 1.

    Here, we distinguish between the terminologies of aspect category extraction and aspect term extraction: The set of possible aspect categories is predefined and rather small (e.g. Price, Battery, Accessories, Display, Portability, Camera), while aspect terms can take many shapes (e.g. “sake menu”, “wine selection” or “French Onion soup”).

  2. 2.

    Parts of a sentence that refer to an aspect of the product, event, entity, etc.

  3. 3.

    For a more convenient notation, we use words and their respective indices interchangeably.

  4. 4.

    We use the euclidean vector distance as a distance measure.

  5. 5.

    Since this GRU processes the sequence in a reversed direction, the final hidden state is the hidden state for the first word.

  6. 6.

    We exclude annotations with aspect = “NULL”.

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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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Authors and Affiliations

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

    Soufian Jebbara & Philipp Cimiano

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  1. Soufian Jebbara
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  2. Philipp Cimiano
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Correspondence to Soufian Jebbara .

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Editors and Affiliations

  1. IT Systems Engineering, Hasso-Plattner Institute, Potsdam, Germany

    Harald Sack

  2. Leibniz Universität Hannover , Hannover, Germany

    Stefan Dietze

  3. Elsevier B.V. , Amsterdem, The Netherlands

    Anna Tordai

  4. Universität Bonn , Bonn, Germany

    Christoph Lange

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Jebbara, S., Cimiano, P. (2016). Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds) Semantic Web Challenges. SemWebEval 2016. Communications in Computer and Information Science, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-46565-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-46565-4_12

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