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2019 | OriginalPaper | Buchkapitel

AE-CharCNN: Char-Level Convolutional Neural Networks for Aspect-Based Sentiment Analysis

verfasst von : Ulisses Brisolara Corrêa, Ricardo Matsumura Araújo

Erschienen in: Advances in Soft Computing

Verlag: Springer International Publishing

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Abstract

Sentiment Analysis was developed to support individuals in the harsh task of obtaining significant information from large amounts of non-structured opinionated data sources, such as social networks and specialized reviews websites. A yet more challenging task is to point out which part of the target entity is addressed in the opinion. This task is called Aspect-Based Sentiment Analysis. The majority of work focuses on coping with English text in the literature, but other languages lack resources, tools, and techniques. This paper focuses on Aspect-Based Sentiment Analysis for Accommodation Services Reviews written in Brazilian Portuguese. Our proposed approach uses Convolution Neural Networks with inputs in Character-level. Results suggest that our approach outperforms lexicon-based and LSTM-based approaches, displaying state-of-the-art performance for binary Aspect-Based Sentiment Analysis.

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Vorheriges Kapitel Advanced Transfer Learning Approach for Improving Spanish Sentiment Analysis

Nächstes Kapitel Understanding the Criminal Behavior in Mexico City through an Explainable Artificial Intelligence Model

This work was developed using PyTorch 1.0.

Pre-trained English Glove models can be obtained from http://nlp.stanford.edu/projects/glove/.

Pre-trained Brazilian Portuguese Glove models can be obtained from http://nilc.icmc.usp.br/embeddings.

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Titel: AE-CharCNN: Char-Level Convolutional Neural Networks for Aspect-Based Sentiment Analysis
verfasst von: Ulisses Brisolara Corrêa
Ricardo Matsumura Araújo
Verlag: Springer International Publishing
Buch: Advances in Soft Computing
Print ISBN: 978-3-030-33748-3

Electronic ISBN: 978-3-030-33749-0

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-33749-0_11

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