Attention-based LSTM for Aspect-level Sentiment Classification

By Yequan Wang, Minlie Huang, Xiaoyan Zhu, Li Zhao. 2016

2,183 citations recorded by the source corpus.

Aspect-level sentiment classification is a fine-grained task in sentiment analysis. Since it provides more complete and in-depth results, aspect-level sentiment analysis has received much attention these years. In this paper, we reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect. For instance, “The appetizers are ok, but the service is slow.”, for aspect taste, the polarity is positive while for service, the polarity is negative. Therefore, it is worthwhile to explore the connection between an aspect and the content of a sentence. To this end, we propose an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification. The attention mechanism can concentrate on different parts of a sentence when different aspects are taken as input. We experiment on the SemEval 2014 dataset and results show that our model achieves state-ofthe-art performance on aspect-level sentiment classification.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

The Risk of Racial Bias in Hate Speech Detection (2019, methodology): “Using a basic neural attention architecture (Wang et al., 2016; Yang et al., 2016), we train a classiﬁer initialized with GloVe vectors (Penning-ton et al., 2014) to minimize the cross-entropy of the annotated class conditional on text, x : with h = f ( x ) , where f is a BiLSTM with attention,…”
Quantifying Attention Flow in Transformers (2020, background): “…the easiest and most popular approach to interpret a model’s decisions and to gain insights about its internals (Vaswani et al., 2017; Xu et al., 2015; Wang et al., 2016; Lee et al., 2017; Dehghani et al., 2019; Rockt¨aschel et al., 2016; Chen and Ji, 2019; Coenen et al., 2019; Clark et al., 2019).”
Recent Trends in Deep Learning Based Natural Language Processing (2017, methodology): “In aspect-based sentiment analysis, (Wang et al., 2016) proposed an attention-based solution where they used aspect embeddings to provide additional support during the classification (Fig.”
Is Attention Interpretable? (2019, methodology): “Since attention calculates a distribution over inputs, prior work has used attention as a tool for interpretation of model decisions (Wang et al., 2016; Lee et al., 2017; Lin et al., 2017; Ghaeini et al., 2018).”
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (2019, methodology): “The earliest work on (T)ABSA relied heavily on feature engineering (Wagner et al., 2014; Kiritchenko et al., 2014), and subsequent neural network-based methods (Nguyen and Shirai, 2015; Wang et al., 2016; Tang et al., 2015, 2016; Wang et al., 2017) achieved higher accuracy.”
Transformation Networks for Target-Oriented Sentiment Classification (2018, methodology): “One method could be concatenating the target representation with each word representation, but the effect as shown in (Wang et al., 2016) is limited.”
Multi-grained Attention Network for Aspect-Level Sentiment Classification (2018, methodology): “ATAE-LSTM (Wang et al., 2016) learns attention embeddings and combine them with the LSTM hidden states to predict the polarity.”
Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, methodology): “[100] proposed an attentionbased solution where they used aspect embeddings to provide additional support during classification (Fig.”
Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021, methodology): “To judge the sentiment of a particular aspect, previous studies (Wang et al., 2016; Tang et al., 2016a; Ma et al., 2017; Chen et al., 2017; Fan et al., 2018; Huang et al., 2018; Gu et al., 2018) have proposed various recurrent neural networks (RNNs) with attention mechanisms to generate aspect-speci”
A General Survey on Attention Mechanisms in Deep Learning (2022, background): “After Bahdanau et al. [3] emphasized the advantages of attention, the attention techniques were reﬁned [4] and quickly became popular for a variety of tasks, such as text classiﬁcation [5], [6], image captioning [7], [8], sentiment analysis [6], [9], and speech recognition [10], [11], [12].”
Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020, background): “Most recent efforts (Wang et al., 2016b; Li et al., 2017; Ma et al., 2017; Fan et al., 2018) resort to assorted attention mechanisms to achieve this goal and have reported appealing results.”
Lifelong Learning Memory Networks for Aspect Sentiment Classification (2018, methodology): “SemEval 2014 [34] is a benchmark dataset that has been used in related studies [2], [3].”
Natural Language Processing Advancements By Deep Learning: A Survey (2020, background): “For aspect-level sentiment classification, attention-based LSTMs are proposed to connect the aspect and sentence content for sentiment classification [125].”
LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation (2018, background): “…2010; Peters et al., 2018), slot ﬁlling (Mesnil et al., 2015; Vu et al., 2016b), machine translation (Bahdanau et al., 2014), sentiment analysis (Wang et al., 2016; Tang et al., 2015), semantic textual similarity (Mueller and Thyagarajan, 2016; Gupta et al., 2018a) and dynamic topic modeling…”