Journal ArticleDOI
Knowledge Guided Capsule Attention Network for Aspect-Based Sentiment Analysis
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TLDR
The proposed knowledge guided capsule network (KGCapsAN) implements the routing method by attention mechanism, and the results show that the proposed method yields the state-of-the-art.Abstract:
Aspect-based (aspect-level) sentiment analysis is an important task in fine-grained sentiment analysis, which aims to automatically infer the sentiment towards an aspect in its context Previous studies have shown that utilizing the attention-based method can effectively improve the accuracy of the aspect-based sentiment analysis Despite the outstanding progress, aspect-based sentiment analysis in the real-world remains several challenges (1) The current attention-based method may cause a given aspect to incorrectly focus on syntactically unrelated words (2) Conventional methods fail to identify the sentiment with the special sentence structure, such as double negatives (3) Most of the studies leverage only one vector to represent context and target However, utilizing one vector to represent the sentence is limited, as the natural languages are delicate and complex In this paper, we propose a knowledge guided capsule network (KGCapsAN), which can address the above deficiencies Our method is composed of two parts, a Bi-LSTM network and a capsule attention network The capsule attention network implements the routing method by attention mechanism Moreover, we utilize two prior knowledge to guide the capsule attention process, which are syntactical and n-gram structures Extensive experiments are conducted on six datasets, and the results show that the proposed method yields the state-of-the-artread more
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TL;DR: A fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis is proposed, which outperforms the state of the art in most cases.
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Systematic reviews in sentiment analysis: a tertiary study
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BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis
TL;DR: This paper proposed a bidirectional emotional recurrent unit for conversational sentiment analysis, where a generalized neural tensor block followed by a two-channel classifier is designed to perform context compositionality and sentiment classification, respectively.
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BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis
TL;DR: This article proposed a party-ignorant framework based on emotional recurrent unit for conversational sentiment analysis, which is suitable for different structures to perform context compositionality and sentiment classification, respectively.
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Unrestricted Attention may not Be All You Need - Masked Attention Mechanism Focuses Better on Relevant Parts in Aspect-Based Sentiment Analysis
TL;DR: This paper proposes a masked attention mechanism customized for ABSA, with two different approaches to generate the mask, which shows significant improvements over state-of-the-art pre-trained language models with full attention, which displays the value of the mask.
References
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TL;DR: BERT as mentioned in this paper pre-trains deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot,Yoshua Bengio +1 more
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Opinion Mining and Sentiment Analysis
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TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
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