INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis
By Sebastian Ruder, Parsa Ghaffari, John Gerard Breslin. 2016
116 citations recorded by the source corpus.
This paper describes our deep learning-based approach to multilingual aspect-based sentiment analysis as part of SemEval 2016 Task 5. We use a convolutional neural network (CNN) for both aspect extraction and aspect-based sentiment analysis. We cast aspect extraction as a multi-label classification problem, outputting probabilities over aspects parameterized by a threshold. To determine the sentiment towards an aspect, we concatenate an aspect vector with every word embedding and apply a convolution over it. Our constrained system (unconstrained for English) achieves competitive results across all languages and domains, placing first or second in 5 and 7 out of 11 language-domain pairs for aspect category detection (slot 1) and sentiment polarity (slot 3) respectively, thereby demonstrating the viability of a deep learning-based approach for multilingual aspect-based sentiment analysis.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Devamanyu Hazarika
- Soujanya Poria
- Erik Cambria
- Tom Young
- Navonil Majumder
- Louis-philippe Morency
- P. Prasad
- Abeer Alsadoon
Citations in the corpus
- Recent Trends in Deep Learning Based Natural Language Processing (2017, methodology): “(Ruder et al., 2016) applied a CNN where in the input they concatenated an aspect vector with the word embeddings to get competitive results.”
- Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning (2017, neutral): “More formally, for any two models A and B combined in an ensemble, we train the following mechanism using Stochastic Gradient Descent: (Bethard et al., 2016) 64.6% NTNUSentEval (Jahren et al., 2016) 64.3% UniPI (Attardi & Sartiano, 2016) 63.9% CUFE (Nabil et al., 2016) 63.7% INSIGHT-1 (Ruder et al.,”
- Aspect Based Sentiment Analysis with Gated Convolutional Networks (2018, methodology): “On each dataset, we duplicate each sentence n a times, which is equal to the number of associated aspect categories (ACSA) or aspect terms (ATSA) (Ruder et al., 2016b,a).”
- Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review (2019, methodology): “7 Ruder et al. (2016) Mobile phone DU CNN + concatenated vectors F1: 45.”
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, methodology): “[49] applied a CNN where in the input they concatenated an aspect vector with the word embeddings to get competitive results.”