Challenge: Existing approaches to model aspect-level sentiment analysis neglect the position information of aspect terms.
Approach: They propose a position-aware bidirectional attention network (PBAN) that mutually models the relation between aspect terms and sentences by employing bidirectional mechanism.
Outcome: The proposed model can distinguish sentiment polarity of aspect terms in sentences . it can also model relation between aspect terms and sentences based on position information .

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Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks (D19-1)

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Challenge: Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence.
Approach: They propose a target-dependent graph attention network for aspect level sentiment classification . it explicitly utilizes the dependency relationship among words to propagate sentiment features . they show that using BERT representations further substantially boosts the performance .
Outcome: The proposed method outperforms baselines with GloVe embeddings and improves with BERT representations.
Attention Transfer Network for Aspect-level Sentiment Classification (2020.coling-main)

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Challenge: Aspect-level sentiment classification aims to detect the sentiment polarity of a given opinion target in a sentence.
Approach: They propose a novel attention transfer network which can exploit attention from document-level sentiment datasets to improve the attention capability of the aspect-level classification task.
Outcome: The proposed method outperforms state-of-the-art methods on two ASC benchmark datasets.
Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020.acl-main)

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Challenge: Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews.
Approach: They propose a relational graph attention network to encode a tree structure for sentiment prediction.
Outcome: The proposed approach improves the performance of the graph attention network (GAT) on the SemEval 2014 and Twitter datasets.
Effective Attention Modeling for Aspect-Level Sentiment Classification (C18-1)

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Challenge: Aspect-level sentiment classification aims to determine sentiment polarity of review sentence towards opinion target . main challenge is to separate different opinion contexts for different targets .
Approach: They propose a method that captures the semantic meaning of the opinion target and a model that incorporates syntactic information into the attention mechanism.
Outcome: The proposed method captures the semantic meaning of the opinion target and incorporates syntactic information into the attention mechanism.
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification (2020.coling-main)

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Challenge: Existing approaches to aspect-level sentiment classification focus on modeling the relationship between aspect words and their contexts with attention, and ignore the use of elaborate knowledge implicit in the context.
Approach: They exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better.
Outcome: The proposed model can model the interaction between the context and aspect words better by using syntactic awareness and external pre-training knowledge.
Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis (2020.aacl-main)

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Challenge: Recent studies ignored the syntactic relationship between the aspect and its corresponding context words, leading the model to focus on syntaktically unrelated words mistakenly.
Approach: They propose to extend the graph convolutional network by assigning different weights to edges of connected words.
Outcome: The proposed method can improve on five datasets showing that it learns and exploits multiword relations and draws different weights of words to improve performance.
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network (P19-1)

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Challenge: Existing studies on aspect sentiment classification focus on non-interactive reviews . a new task aims to predict sentiment polarities for specific aspects from interactive reviews based on annotated corpus .
Approach: They propose a task to predict aspects from interactive QA style reviews using an annotated corpus.
Outcome: The proposed approach is compared with state-of-the-art methods against a high-quality corpus of data.
Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis (P19-2)

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Challenge: End-to-end deep learning systems lack flexibility as one cannot adjust the network to fix an obvious problem.
Approach: They propose a way to leverage lexicon information to make the model more flexible . they also explore the effect of regularizing attention vectors to allow the network to have a broader "focus"
Outcome: The proposed approach leverages lexicon information to make it more flexible and robust.
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification (D19-1)

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Challenge: Existing methods for aspect-level sentiment classification are limited for dealing with overlapped features.
Approach: They propose to use capsule network to construct vector-based feature representation and cluster features by an EM routing algorithm to model semantic relationship between aspect terms and context.
Outcome: The proposed model achieves state-of-the-art on three datasets.
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (D19-1)

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Challenge: Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet.
Approach: They propose a method to identify sentiment polarity of opinion words on a specific aspect of a sentence using neural networks.
Outcome: The proposed method is the state-of-the-art in aspect-based sentiment classification.

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