Aspect-based Sentiment Classification Research Articles

Filter channel network based on contextual position weight for aspect-based sentiment classification

Filter channel network based on contextual position weight for aspect-based sentiment classification

Cross-Domain Aspect-Based Sentiment Classification with a Pre-Training and Fine-Tuning Strategy for Low-Resource Domains

Aspect-based sentiment classification (ABSC) is a crucial sub-task of fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspects in a sentence as positive, negative, or neutral. Most existing ABSC methods are based on supervised learning. However, these methods rely heavily on fine-grained labeled training data, which can be scarce in low-resource domains, limiting their effectiveness. To overcome this challenge, we propose a low-resource cross-domain aspect-based sentiment classification (CDABSC) approach based on a pre-training and fine-tuning strategy. This approach applies the pre-training and fine-tuning strategy to an advanced deep learning method designed for ABSC, namely the attention-based encoding graph convolutional network (AEGCN) model. Specifically, a high-resource domain is selected as the source domain, and the AEGCN model is pre-trained using a large amount of fine-grained annotated data from the source domain. The optimal parameters of the model are preserved. Subsequently, a low-resource domain is used as the target domain, and the pre-trained model parameters are used as the initial parameters of the target domain model. The target domain is fine-tuned using a small amount of annotated data to adapt the parameters to the target domain model, improving the accuracy of sentiment classification in the low-resource domain. Finally, experimental validation on two domain benchmark datasets, restaurant and laptop, demonstrates significant outperformance of our approach over the baselines in CDABSC Micro-F1.

A dual contrastive learning-based graph convolutional network with syntax label enhancement for aspect-based sentiment classification

Introduction: Aspect-based sentiment classification is a fine-grained sentiment classification task. State-of-the-art approaches in this field leverage graph neural networks to integrate sentence syntax dependency. However, current methods fail to exploit the data augmentation in encoding and ignore the syntactic relation in sentiment delivery.Methods: In this work, we propose a novel graph neural network-based architecture with dual contrastive learning and syntax label enhancement. Specifically, a contrastive learning-based contextual encoder is designed, integrating sentiment information for semantics learning. Moreover, a weighted label-enhanced syntactic graph neural network is established to use both the syntactic relation and syntax dependency, which optimizes the syntactic weight between words. A syntactic triplet between words is generated. A syntax label-based contrastive learning scheme is developed to map the triplets into a unified feature space for syntactic information learning.Results: Experiments on five publicly available datasets show that our model substantially outperforms the baseline methods.Discussion: As such, the proposed method shows its effectiveness in aspect-based sentiment classification tasks.

DRGAT: Dual-relational graph attention networks for aspect-based sentiment classification

Aspect-based sentiment classification has become a popular topic in natural language processing. Exploiting dependency syntactic information with graph neural networks has recently become a popular trend. Despite their success, methods that rely heavily on a dependency tree face major challenges. This concerns the alignment of aspects and their word sentiments due to the richness of the language and the fact that a dependency tree might produce noisy signals from unrelated associations. This paper introduces a Dual-Relational Graph Attention Network (DRGAT) that fully exploits syntactic structural information and then the sentiment-aware context (e.g., phrase segmentation and hierarchical structure) of the constituent tree of a sentence. Additional constituency and dependency attention mechanisms provide comprehensive syntactic information across words, thereby enabling an accurate connection between aspect words and corresponding sentiment words. Considering that the original parsed constituency tree may have a large depth, this could lead to words being far apart increasing the computational overhead. The constituency tree of each sentence is dynamically reconstructed by determining the importance of each relational node. Extensive experimental results on six English datasets demonstrated that fully exploiting syntactic information can achieve excellent sentiment classification results.

An Efficient Aspect-based Sentiment Classification with Hybrid Word Embeddings and CNN Framework

Background: As the e-commerce product reviews and social media posts are increasing enormously, the size of the database for polarity/ sentiment detection is a challenging task, and again, predicting polarities associated with respect to aspect terms end to end in a sentence is a havoc in real-time applications. Human behavior is influenced by the various opinions generated in society. Public opinion influences our decisions most often. Businesses and establishments always need to collect the opinion of the society, which they try to obtain using customer feedback forms and questionnaires or surveys, which help them to be aware of the shortcomings if any, and to use suggestions to improve quality. It works in the same way for customers as well and the opinions of other customers about a particular product can come in handy when deciding to buy a product. Objectives: In this work, an efficient Aspect-based Sentiment Classification technique has been introduced with a hybrid, multiple-word embedding methods and implemented using the CNN framework on large databases. Methods: Most of the traditional models have a limitation on the dependency for one or more similar types of aspect words for sentiment classification problem. However, these conventional models such as TF-ID, Word 2Vec and Glove method consumes much more time for word embedding process and Aspect terms generation and further process of aspect level sentiment classification. Further, these models are facing problems of high true negative rate and misclassification rate on large aspect databases in sentiment classification. In this article, we have introduced an efficient Proposed ensemble word embedding model in the CNN network and defined Hybrid Word2 Vec method, Hybrid Glove word embedding method and Hybrid Random Forest model for sentiment classification. Results: Experiments on a widely used benchmark prove that the proposed word embedding method- based classification technique results in to higher true positive rate with minimal misclassifications and also supports better runtime and accuracy than the traditional word embedding-based aspect level classification approaches. Conclusion: In this article, a hybrid ensemble feature ranking-based classification model is proposed on the large aspect databases. In this work, advanced multiple-word embedding methods are implemented to improve the essential feature extraction problem in the aspect level sentiment process. These multiple-word embedding methods are applied to the sentiment databases in the CNN framework.

CausalABSC: Causal Inference for Aspect Debiasing in Aspect-Based Sentiment Classification

CausalABSC: Causal Inference for Aspect Debiasing in Aspect-Based Sentiment Classification

Cross-Domain Aspect-based Sentiment Classification with Tripartite Graph Modeling

Cross-Domain Aspect-based Sentiment Classification with Tripartite Graph Modeling

Graph Convolutional Network with Interactive Memory Fusion for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of a specified aspect in a sentence. Graph neural networks (GNN) based on dependency trees have been shown to be effective for ABSA by explicitly modeling the connection between aspect and opinion terms and exploiting local semantic and syntactic information in the sentence. However, most previous works have overlooked the use of global dependency information. In this paper, we propose a novel Graph Convolutional Network (GCN) with an Interactive Memory Fusion (IMF) mechanism (IMF-GCN) that incorporates both global and local structural information for aspect-based sentiment classification. The IMF mechanism efficiently fuses global and local structural dependency information by assigning different weights to global and local dependency modules. Syntactic constraints are also imposed to prevent the graph convolution propagation unrelated to the target words, further improving the model’s performance. The evaluation metrics used in the paper are accuracy and macro-average F1 scores, and the proposed approach achieves optimal results on three datasets with F1 scores of 79.60%, 82.19%, and 77.75%, which outperform the baseline model.

Employing synthetic data for addressing the class imbalance in aspect-based sentiment classification

ABSTRACT The class imbalance problem, in which the distribution of different classes in training data is unequal or skewed, is a prevailing problem. This can lead to classifier algorithms being biased, negatively impacting the performance of the minority class. In this paper, we addressed the class imbalance problem in datasets for aspect-based sentiment classification. Aspect-based Sentiment Classification (AbSC) is a type of fine-grained sentiment analysis in which sentiments about particular aspects of an entity are extracted. In this work, we addressed the issue of class imbalance by creating synthetic data. For synthetic data generation, two techniques have been proposed: paraphrasing using the PEGASUS fine-tuned model and backtranslation using the M2M100 neural machine translation model. We compared these techniques with two other class balancing techniques, such as weighted oversampling and cross-entropy loss with class weight. An extensive experimental study has been conducted on three benchmark datasets for restaurant reviews: SemEval-2014, SemEval-2015, and SemEval-2016. We applied these methods to the BERT-based deep learning model for aspect-based sentiment classification and studied the effect of balancing the data on the performance of these models. Our proposed balancing technique, using synthetic data, yielded better results than the other two existing methods for dealing with multi-class imbalance.

Performance analysis of aspect-level sentiment classification task based on different deep learning models.

Aspect-level sentiment classification task (ASCT) is a natural language processing task that aims to correctly identify specific aspects and determine their sentiment polarity from a given target sentence. Deep learning models have been proven to be effective in aspect-based sentiment classification tasks, and the mainstream Aspect-level sentiment classification (ASC) models currently constructed generally assume that the training and test datasets are Gaussian distribution (e.g., the same language). Once the data distribution changes, the ASC model must be retrained on the new distribution data to achieve good performance. However, acquiring a large amount of labeled data again typically requires a lot of manpower and money, which seems unlikely, especially for the ASC task, as it requires aspect-level annotation. This article analyzes the performance of sequence-based models, graph-based convolutional neural networks, and pre-training language models on the aspect-level sentiment classification task using two sets of comment datasets in Chinese and English, from four perspectives: classification performance, performance with different aspect numbers, specific case performance, and computational cost. In this article, we design a state-of-the-art ASC-based classification method and conduct a systematic study on eight public standard English and Chinese datasets with various commonly used assessment measures that provide directions for cross-language migration. Finally, we discuss the limitations of the study as well as future research directions.

PHNN: A Prompt and Hybrid Neural Network-Based Model for Aspect-Based Sentiment Classification

Aspect-based sentiment classification (ABSC) is an important task in natural language processing (NLP) that aims to predict the sentiment polarity of different aspects in a sentence. The attention mechanism and pre-trained models are commonly used in ABSC tasks. However, a single pre-trained model typically does not perceive downstream tasks very well, and the attention mechanism usually neglects the syntactic information of sentences. In this paper, we propose a prompt and hybrid neural network (PHNN) model, which utilizes the prompt and a hybrid neural network structure to solve the ABSC task. More precisely, it first uses the prompt to convert an input sentence into cloze-type text and utilizes RoBERTa to deal with the input. Then, it applies the graph convolutional neural network (GCN) combined with the convolutional neural network (CNN) to extract the syntactic features of the sentence while using bi-directional long short-term memory (BiLSTM) to obtain the semantic features of the sentence. Further, it utilizes the multi-head attention (MHA) mechanism to learn attention in the sentence and aspect words. Finally, the sentiment polarity of the aspect words is obtained by using the softmax function. Experiments on three benchmark datasets show that PHNN has the best performance compared with other baselines, validating the efficiency of our model.

Heterogeneous Reinforcement Learning Network for Aspect-Based Sentiment Classification With External Knowledge

Aspect-based sentiment classification aims to automatically predict the sentiment polarity of the specific aspect in a text. However, it is challenging to confirm the mapping between the aspect and the core context since a number of existing methods concentrate on building the global relations of the full context rather than the partial connections based on the aspects. Motivated by the fundamental insights of reinforcement learning, we propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>H</b></u> eterogeneous <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>R</b></u> einforcement <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>L</b></u> earning <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>N</b></u> etwork for aspect-based sentiment analysis (HRLN) to alleviate these issues, which contains two primary components, a heterogeneous network module, and a knowledge graph-based reinforcement learning module consistent with common-sense knowledge and emotional knowledge. To evaluate the effectiveness of HRLN, we conduct extensive experiments on five benchmark datasets, which indicate that HRLN achieves competitive performance and yields state-of-the-art results on all datasets. Additionally, we present an intuitive comprehension of why our HRLN model is more robust for aspect-based sentiment classification via case studies.

Cross-Domain Aspect-Based Sentiment Classification by Exploiting Domain- Invariant Semantic-Primary Feature

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this paper, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods.

DIWS-LCR-Rot-hop++: A Domain-Independent Word Selector for Cross-Domain Aspect-Based Sentiment Classification

The Aspect-Based Sentiment Classification (ABSC) models often suffer from a lack of training data in some domains. To exploit the abundant data from another domain, this work extends the original state-of-the-art LCR-Rot-hop++ model that uses a neural network with a rotatory attention mechanism for a cross-domain setting. More specifically, we propose a Domain-Independent Word Selector (DIWS) model that is used in combination with the LCR-Rot-hop++ model (DIWS-LCR-Rot-hop++). DIWS-LCR-Rot-hop++ uses attention weights from the domain classification task to determine whether a word is domain-specific or domain-independent, and discards domain-specific words when training and testing the LCR-Rot-hop++ model for cross-domain ABSC. Overall, our results confirm that DIWS-LCR-Rot-hop++ outperforms the original LCR-Rot-hop++ model under a cross-domain setting in case we impose an optimal domain-dependent attention threshold value for deciding whether a word is domain-specific or domain-independent. For a target domain that is highly similar to the source domain, we find that imposing moderate restrictions on classifying domain-independent words yields the best performance. Differently, a dissimilar target domain requires a strict restriction that classifies a small proportion of words as domain-independent. Also, we observe information loss which deteriorates the performance of DIWS-LCR-Rot-hop++ when we categorize an excessive amount of words as domain-specific and discard them.

MSFNet: modality smoothing fusion network for multimodal aspect-based sentiment analysis

Multimodal aspect-based sentiment classification (MABSC) aims to determine the sentiment polarity of a given aspect in a sentence by combining text and image information. Although the text and the corresponding image in a sample are associated with aspect information, their features are represented in distinct semantic spaces, creating a substantial semantic gap. Previous research focused primarily on identifying and fusing aspect-level sentiment expressions of different modalities while ignoring their semantic gap. To this end, we propose a novel aspect-based sentiment analysis model named modality smoothing fusion network (MSFNet). In this model, we process the unimodal aspect-aware features via the feature smoothing strategy to partially bridge modality gap. Then we fuse the smoothed features deeply using the multi-channel attention mechanism, to obtain aspect-level sentiment representation with comprehensive representing capability, thereby improving the performance of sentiment classification. Experiments on two benchmark datasets, Twitter2015 and Twitter2017, demonstrate that our model outperforms the second-best model by 1.96% and 0.19% in terms of Macro-F1, respectively. Additionally, ablation studies provide evidence supporting the efficacy of each of our proposed modules. We release the code at: https://github.com/YunjiaCai/MSFNet.

A Context-focused Attention Evolution Model for Aspect-based Sentiment Classification

Due to their inherent capability in the semantic alignment of aspects and their context words, Attention and Long-Short-Term-Memory (LSTM) mechanisms are widely adopted for Aspect-Based Sentiment Classification (ABSC) tasks. Instead, it is challenging to handle long-range word dependencies on multiple entities due to the deficiency in attention mechanisms. To solve this problem, we propose a Context-Focused Aspect-Based Network to align attention before LSTM, making the model focus more on aspect-related words and ignore irrelevant words, improving the accuracy of final classification. This can either alleviate attention distraction or reinforce the text representation ability. Experiments on two benchmark datasets show that the results achieve respectable performance compared to the state-of-the-art methods available in ABSC. Our approach has the potential to improve classification accuracy by adaptively adjusting the focus on context.

Diagnostic Classifiers for Explaining a Neural Model with Hierarchical Attention for Aspect-based Sentiment Classification

The current models proposed for aspect-based sentiment classification (ABSC) are mainly developed with the purpose of providing high rates of accuracy, regardless of the inner working which is usually difficult to understand. Considering the state-of-art model LCR-Rot-hop++ for ABSC, we use diagnostic classifiers to gain insights into the encoded information of each layer. Starting from a set of various hypotheses, we test how sentiment-related information is captured by different layers of the model. Given the model architecture, information about the related words to the target is easily extracted. Also, the model is able to detect to some extent information about the sentiments of the words and, in particular, sentiments of the words related to the target. However, the model is less effective in extracting the aspect mentions associated with a word and the general structure of the sentence.

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

Aspect-Based Sentiment Analysis (ABSA) on Arabic corpus has become an active research topic in recent days. ABSA refers to a fine-grained Sentiment Analysis (SA) task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text. Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons. In literature, concerning the Arabic language text analysis, the authors made use of regular Machine Learning (ML) techniques that rely on a group of rare sources and tools. These sources were used for processing and analyzing the Arabic language content like lexicons. However, an important challenge in this domain is the unavailability of sufficient and reliable resources. In this background, the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification (BROFDL-AASC) technique. The aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic language. In the presented BROFDL-AASC model, data pre-processing is performed at first to convert the input data into a useful format. Besides, the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine (DFRBM) model for the identification and categorization of sentiments. Furthermore, the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM model. This scenario establishes the novelty of current study. The performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

Attention-Based Multi-Channel Gated Recurrent Neural Networks: A Novel Feature-Centric Approach for Aspect-Based Sentiment Classification

Attention-Based Multi-Channel Gated Recurrent Neural Networks: A Novel Feature-Centric Approach for Aspect-Based Sentiment Classification