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2016 | OriginalPaper | Chapter

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

Authors : Soufian Jebbara, Philipp Cimiano

Published in: Semantic Web Challenges

Publisher: Springer International Publishing

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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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previous chapter A Knowledge-Based Approach for Aspect-Based Opinion Mining

next chapter 6th Open Challenge on Question Answering over Linked Data (QALD-6)

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”).

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

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

We use the euclidean vector distance as a distance measure.

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

We exclude annotations with aspect = “NULL”.

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Title: Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
Authors: Soufian Jebbara
Philipp Cimiano
Publisher: Springer International Publishing
Book: Semantic Web Challenges
Print ISBN: 978-3-319-46564-7

Electronic ISBN: 978-3-319-46565-4

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-46565-4_12

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