Aspect-based Sentiment Analysis Research Articles

Graph Convolutional Network with Syntactic Dependency for Aspect-Based Sentiment Analysis

AbstractAspect-based sentiment analysis (ABSA) aims to mine the sentiment tendencies expressed by specific aspect terms. The studies of ABSA mainly focus on the attention-based approaches and the graph neural network approaches based on dependency trees. However, the attention-based methods usually face difficulties in capturing long-distance syntactic dependencies. Additionally, existing approaches using graph neural networks have not made sufficient exploit the syntactic dependencies among aspects and opinions. In this paper, we propose a novel Syntactic Dependency Graph Convolutional Network (SD-GCN) model for ABSA. We employ the Biaffine Attention to model the sentence syntactic dependencies and build syntactic dependency graphs from aspects and emotional words. This allows our SD-GCN to learn both the semantic relationships of aspects and the overall semantic meaning. According to these graphs, the long-distance syntactic dependency relationships are captured by GCNs, which facilitates SD-GCN to capture the syntactic dependencies between aspects and viewpoints more comprehensively, and consequently yields enhanced aspect features. We conduct extensive experiments on four aspect-level sentiment datasets. The experimental results show that our SD-GCN outperforms other methodologies. Moreover, ablation experiments and visualization of attention further substantiate the effectiveness of SD-GCN.

A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification

Aspect based sentiment analysis (ABSA) manifests the well refined work as Aspect-level sentiment classification (ASC) due to recent high attention and profound outcomes. This paper reveals the exorbitant relation of concerned aspect in determining the sentiment polarity of a sentence in addition to their content. Considering an instance, “The food is tasty but restaurant is untidy”, aspect food reveals the polarity as positive, while in case of restaurant, the polarity seems negative. Hence to scout relation between an aspect\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$aspect$$\\end{document} and context\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$context$$\\end{document} words present in sentence is much vital. In spite of the exceptional progress, the ASC experiences certain pitfalls as (1) The current attention based methods makes the given aspect to falsely relate syntactically unrelated words as related ones. (2) Single context-independent representation is only achieved by traditional Word2Vec or GloVe based embedding vectors. (3) Sentiments for multiple words that are inconsecutive remain insufficient for CNN based models. A Sparse-Self-Attention-based Gated Recurrent Unit with Aspect Embedding (SSA-GRU-AE) implementing BERT for ASC is proposed to solve these issues. The proposed SSA-GRU-AE mechanism is centralized on various portions of sentence as multiple aspects are taken as input. The experimental analysis on ASC datasets has proved that proposed model enhanced performance on ASC.

Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi

Objectives: Aspect terms play a vital role in finalizing the sentiment of a given review. This experimental study aims to improve the aspect term extraction mechanism for Hindi language reviews. Methods: We trained and evaluated a deep learning-based supervised model for aspect term extraction. All experiments are performed on a well-accepted Hindi dataset. A BiLSTM-based attention technique is employed to improve the extraction results. Findings: Our results show better F-score results than many existing supervised methods for aspect term extraction. Accuracy results are outstanding compared to other reported results. Results showed an outstanding 91.27% accuracy and an F–score of 43.16. Novelty: This proposed architecture and the achieved results are a foundational resource for future studies and endeavours in the field. Keywords: Sentiment analysis, Aspect based sentiment analysis, Aspect term extraction, Deep Learning, Bi LSTM, Indian language, Hindi

Bidirectional Complementary Correlation-Based Multimodal Aspect-Level Sentiment Analysis

Aspect-based sentiment analysis is the key to natural language processing, and it focuses on the polarity of emotions associated with specific text aspects. Traditional models that combine text and visual data tend to ignore the deeper interconnections between patterns. To solve this problem, the authors propose a multimodal sentiment-oriented analysis (BiCCM-ABSA) model based on bidirectional complementary correlation. The model utilizes text-image synergy through a novel cross-modal attention mechanism to align text with image features. With the transformer architecture, it is not only a simple fusion, but also ensures the complex alignment of multi-modal features and gating mechanisms. Experiments were conducted on the Twitter-15 and Twitter-17 datasets, achieving 69.28 accuracy and 67.54% F1 score, respectively. The experimental results demonstrate the advantages of BiCCM-ABSA, the bidirectional approach of the model and the effective cross-modal correlation set a new benchmark in the field of multimodal emotion recognition, providing insights beyond traditional single-modal analysis.

Autoregressive Feature Extraction with Topic Modeling for Aspect-based Sentiment Analysis of Arabic as a Low-resource Language

This paper proposes an approach for aspect-based sentiment analysis of Arabic social data, especially the considerable text corpus generated through communications on X (formerly known as Twitter) for expressing opinions in Arabic-language tweets during the COVID-19 pandemic. The proposed approach examines the performance of several pre-trained predictive and autoregressive language models; namely, Bidirectional Encoder Representations from Transformers (BERT) and XLNet, along with topic modeling algorithms; namely, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), for aspect-based sentiment analysis of online Arabic text. In addition, Bidirectional Long Short Term Memory (Bi-LSTM) deep learning model is used to classify the extracted aspects from online reviews. Obtained experimental results indicate that the combined XLNet-NMF model outperforms other implemented state-of-the-art methods through improving the feature extraction of unstructured social media text with achieving values of 0.946 and 0.938, for average sentiment classification accuracy and F-measure, respectively.

Imbalance Dataset in Aspect-Based Sentiment Analysis on Game Genshin Impact Review

Sentiment analysis was commonly used to determine the polarity of the review text. However, there is a problem if some reviews have more than one aspect with different polarities, so the reviews have more than one polarity. That has happened in some reviews on the game Genshin Impact. Not merely that, the number of sentiments contained in a review is not always the same as other reviews will cause imbalanced data. So, this study will handle imbalance data with Random Under-Sampling and Random Over-Sampling on aspect-based-sentiment-analysis of Genshin Impact Review with Multinomial Naïve-Bayes, so that the classification prediction does not ignore the minority class due to the dominance of the majority class. The classification process used K-Fold Cross Validation (k=10) validation method and the Laplace smoothing technique on Multinomial Naïve Bayes. As a result, the conclusion is that Random Oversampling had better accuracy than Random Undersampling in handling imbalanced data on aspect-based sentiment analysis of Genshin Impact game Review in Indonesian with Naïve Bayes Multinomial, with the highest accuracy of 85.55%.

Enhanced Syntactic and Semantic Graph Convolutional Network With Contrastive Learning for Aspect-Based Sentiment Analysis

Enhanced Syntactic and Semantic Graph Convolutional Network With Contrastive Learning for Aspect-Based Sentiment Analysis

Learning Polarity Embedding Attention for Aspect-based Sentiment Analysis

The primary goal of Sentiment Analysis (SA) is to recognize the emotions present in natural language text. Generally, in opinion content, emotions are often driven by several aspects of their interests. Any SA task that groups data into various aspects and identifies sentiments is referred to as Aspect-Based Sentiment Analysis (ABSA). Recent advances in Deep Learning (DL) have brought revolutionary changes in the performance of Machine Learning models. Their ability to capture semantic and syntactic traits of any intrinsic data model is highly appreciated. In this research work, we use DL techniques to address the challenges of ABSA aiming to improve sentiment granularity at the aspect level. The proposed methodology works in two stages: (i) aspect terms extraction and (ii) sentiment polarity classification. The task of aspect terms extraction is achieved through the concept of Named-Entity Recognition (NER). However, most of the available NER models are domain dependent and utilize hand-crafted features for learning labeled data. Hence, for aspect terms extraction, a joint model based on Bi-GRU and Conditional Random Fields (CRF) is proposed. Similarly, for sentiment polarity classification, we introduce a novel attention-based neural network called Polarity Embedded Attention Network (PEAN). The intuition behind the PEAN is that, when an aspect term appears in a sentence, its related sentiment term is represented by the polarity embedding. Hence, PEAN combines sentence embedding with aspect and polarity embedding to learn the relationship between sentence and aspect terms. The effectiveness of the proposed model is realized through a comparative study of different models on benchmark datasets. It yields better results compared to other baseline techniques.

Metric-Free Learning Network with Dual Relations Propagation for Few-Shot Aspect Category Sentiment Analysis

Abstract Few-shot Aspect Category Sentiment Analysis (ACSA) is a crucial task for aspect-based sentiment analysis, which aims to detect sentiment polarity for a given aspect category in a sentence with limited data. However, few-shot learning methods focus on distance metrics between the query and support sets to classify queries, heavily relying on aspect distributions in the embedding space. Thus, they suffer from overlapping distributions of aspect embeddings caused by irrelevant sentiment noise among sentences with multiple sentiment aspects, leading to misclassifications. To solve the above issues, we propose a metric-free method for few-shot ACSA, which models the associated relations among the aspects of support and query sentences by Dual Relations Propagation (DRP), addressing the passive effect of overlapping distributions. Specifically, DRP uses the dual relations (similarity and diversity) among the aspects of support and query sentences to explore intra-cluster commonality and inter-cluster uniqueness for alleviating sentiment noise and enhancing aspect features. Additionally, the dual relations are transformed from support-query to class-query to promote query inference by learning class knowledge. Experiments show that we achieve convincing performance on few-shot ACSA, especially an average improvement of 2.93% accuracy and 2.10% F1 score in the 3-way 1-shot setting.

Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian Reviews

Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining insights into their practical utility. A comprehensive, multi-level analysis is performed, covering the document, sentence, and aspect levels. The main contributions of the paper refer to the in-depth exploration of multiple sentiment analysis models at three different textual levels and the subsequent improvements brought with respect to these standard models. Additionally, a balanced dataset of Romanian reviews from twelve product categories is introduced. The results indicate that, at the document level, supervised deep learning techniques yield the best outcomes (specifically, a convolutional neural network model that obtains an AUC value of 0.93 for binary classification and a weighted average F1-score of 0.77 in a multi-class setting with 5 target classes), albeit with increased resource consumption. Favorable results are achieved at the sentence level, as well, despite the heightened complexity of sentiment identification. In this case, the best-performing model is logistic regression, for which a weighted average F1-score of 0.77 is obtained in a multi-class polarity classification task with three classes. Finally, at the aspect level, promising outcomes are observed in both aspect term extraction and aspect category detection tasks, in the form of coherent and easily interpretable word clusters, encouraging further exploration in the context of aspect-based sentiment analysis for the Romanian language.

Knowledge-Guided Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Analysis

The purpose of aspect-based sentiment analysis (ABSA) is to determine the sentiment polarity of aspects in a given sentence. Most historical works on sentiment analysis used complex and inefficient methods to integrate external knowledge. Furthermore, they fell short of completely utilizing BERT’s potential because when trying to generate word embeddings, they merely averaged the BERT subword vectors. To overcome these limitations, we propose a knowledge-guided heterogeneous graph convolutional network for aspect-based sentiment analysis (KHGCN). Specifically, we consider merging subword vectors utilizing a dynamic weight mechanism in the BERT embedding layer. Additionally, heterogeneous graphs are constructed to fuse different feature associations between words, and graph convolutional networks are utilized to identify context-specific syntactic features. Furthermore, by embedding a knowledge graph, the model can learn additional features from sources other than the corpus. Based on this knowledge, it is consequently possible to obtain more knowledge representation for a particular aspect by utilizing the attention mechanism. Last but not least, semantic features, syntactic features, and knowledge are dynamically combined using feature fusion. Experiments on three public datasets demonstrate that our model achieves accuracy rates of 80.87%, 85.42%, and 91.07%, which is an improvement of more than 2% compared to other benchmark models based on HGCNs and BERT.

A cross-model hierarchical interactive fusion network for end-to-end multimodal aspect-based sentiment analysis

For the aspect-based sentiment analysis task, traditional works are only for text modality. However, in social media scenarios, texts often contain abbreviations, clerical errors, or grammatical errors, which invalidate traditional methods. In this study, the cross-model hierarchical interactive fusion network incorporating an end-to-end approach is proposed to address this challenge. In the network, a feature attention module and a feature fusion module are proposed to obtain the multimodal interaction feature between the image modality and the text modality. Through the attention mechanism and gated fusion mechanism, these two modules realize the auxiliary function of image in the text-based aspect-based sentiment analysis task. Meanwhile, a boundary auxiliary module is used to explore the dependencies between two core subtasks of the aspect-based sentiment analysis. Experimental results on two publicly available multi-modal aspect-based sentiment datasets validate the effectiveness of the proposed approach.

Multi-strategy text data augmentation for enhanced aspect-based sentiment analysis in resource-limited scenarios

Multi-strategy text data augmentation for enhanced aspect-based sentiment analysis in resource-limited scenarios

Optimizing aspect-based sentiment analysis using sentence embedding transformer, bayesian search clustering, and sparse attention mechanism

The aspect-based sentiment analysis of hotel reviews represents a critical yet underexplored area of research. Such reviews encompass a range of expressions that extend beyond simple positive, negative, or neutral sentiments, delving into specific aspects discussed within the review. For instance, a statement like 'the location is comfortable, the food is good, but unfortunately, the service is bad' encapsulates multiple aspects, including location, food, and service. Existing research in Aspect-Based Sentiment Analysis (ABSA) frequently employs word embedding techniques for feature extraction, utilizing both Term and Transformer models. This study introduces an innovative approach by transitioning from word to sentence embedding, employing the SBERT transformer model for this transformation. This study further innovates label creation and topic modeling by integrating Bayesian search clustering with Inverse Document Frequency (IDF) calculations. This approach enables the identification of relevant topics, categorizing IDF scores based on occurrence frequencies. Scores ranging between 4–6 are marked as having low value but high informative potential, making them suitable candidates for label-specific datasets, particularly those without pre-existing labels. Additionally, this study incorporates the BigBird model to enhance this classification methodology. Applying this comprehensive framework to hotel review datasets, the result of the ABSA evaluation achieved an impressive accuracy score of 99%, indicating the efficacy of the proposed methodology in this nuanced field.

Flexibly utilizing syntactic knowledge in aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) refers to ascertaining the propensity of sentiment expressed in a text towards a particular aspect. While previous models have utilized dependency graphs and GNNs to facilitate information exchange, they face challenges such as smoothing of aspect representation and a gap between word-based dependency graphs and subword-based BERT. Taking into account the above deficiencies, we argue for a new approach called SRE-BERT that flexibly utilizes syntax knowledge to enhance aspect representations by relying on syntax representations. First, we propose a syntax representation encoder to acquire the syntactic vector for each token. Then, we devise a syntax-guided transformer that employs syntax representation to compute multi-head attention, thereby enabling direct syntactic interaction between any two tokens. Finally, the token-level vectors derived from the syntax-guided transformer are employed to enhance the semantic representations obtained by BERT. In addition, during the aforementioned process, we introduced a Masked POS Label Prediction (MPLP) method to pre-train the syntax encoder. A wide range of studies have been undertaken on data collections covering three distinct fields, and the results indicate that our SRE-BERT outperforms the second-ranked model by 1.97%, 1.55%, and 1.20% on the Rest14, Lap14, and Twitter 3 datasets, respectively.

Modeling Structured Dependency Tree with Graph Convolutional Networks for Aspect-Level Sentiment Classification.

Aspect-based sentiment analysis is a fine-grained task where the key goal is to predict sentiment polarities of one or more aspects in a given sentence. Currently, graph neural network models built upon dependency trees are widely employed for aspect-based sentiment analysis tasks. However, most existing models still contain a large amount of noisy nodes that cannot precisely capture the contextual relationships between specific aspects. Meanwhile, most studies do not consider the connections between nodes without direct dependency edges but play critical roles in determining the sentiment polarity of an aspect. To address the aforementioned limitations, we propose a Structured Dependency Tree-based Graph Convolutional Network (SDTGCN) model. Specifically, we explore construction of a structured syntactic dependency graph by incorporating positional information, sentiment commonsense knowledge, part-of-speech tags, syntactic dependency distances, etc., to assign arbitrary edge weights between nodes. This enhances the connections between aspect nodes and pivotal words while weakening irrelevant node links, enabling the model to sufficiently express sentiment dependencies between specific aspects and contextual information. We utilize part-of-speech tags and dependency distances to discover relationships between pivotal nodes without direct dependencies. Finally, we aggregate node information by fully considering their importance to obtain precise aspect representations. Experimental results on five publicly available datasets demonstrate the superiority of our proposed model over state-of-the-art approaches; furthermore, the accuracy and F1-score show a significant improvement on the majority of datasets, with increases of 0.74, 0.37, 0.65, and 0.79, 0.75, 1.17, respectively. This series of enhancements highlights the effective progress made by the STDGCN model in enhancing sentiment classification performance.

Multi-modal online review driven product improvement design based on scientific effects knowledge graph

Online reviews serve as significant channels for users to express their preferences, constituting an essential data source for enterprises to identify product requirements. However, with the widespread adoption of smartphones, the act of capturing spontaneous photographs has become a habitual practice for the majority, resulting in the increasing prevalence of supplementary visual expressions within online reviews. Therefore, an important research question emerges: How can product requirements be effectively extracted from multimodal online reviews and subsequently translated into product design proposals? In this paper, we establish a framework, seamlessly integrating aspect-based sentiment analysis, product requirement identification, and requirement mapping based on a scientific effect knowledge graph. Firstly, we conduct aspect term extraction on the online reviews, followed by aspect sentiment classification. Subsequently, we delve deeper into the analyzed results obtained from aspect-based sentiment analysis to identify preferences in product requirements. Finally, we employ requirement mapping based on a scientific effect knowledge graph to generate proposals for product design improvements. To validate the efficacy of our approach, we conducted experiments and the results demonstrate that our method outperforms alternative approaches, while the requirement mapping based on a scientific effect knowledge graph efficiently facilitates the realisation of product design improvements.

AMIFN: Aspect-guided multi-view interactions and fusion network for multimodal aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA), which aims to analyze users’ sentiment towards the targeted aspect, has recently gained increasing attention due to its importance in supporting corresponding decision-makings in various tasks. Most existing ABSA studies primarily depend on only textual modality, but ignore the fact that in many cases the targeted aspect doesn’t appear in the sentence. Thus, multimodal ABSA is expected to alleviate this dilemma. However, most existing MABSA approaches still suffer from the following limitations: (1) ignoring the possible aspect-image irrelevant issue; (2) ignoring the coarse-grained interaction between the sentence and its associated image; (3) failing to simultaneously leverage multiple types of useful knowledge information. To address these issues, we propose an aspect-guided multi-view interactions and fusion network (AMIFN) for MABSA. Specifically, we utilize the multi-head attention mechanism to generate aspect-guided textual representation, which is used as the extended aspect semantic for guiding the subsequent aspect-related interactions. When exploring aspect-guided visual representation, we employ the image gate to dynamically filter potential noise introduced by the associated image to generate the final image representation. Meanwhile, the coarse-grained sentence-image interaction, which contains context and semantics information, and the syntactic dependencies, are leveraged for graph construction to obtain aspect-guided text-image interaction representations. Finally, the extracted multi-view interaction representations are integrated for sentiment classification. Extensive experimental results on three multimodal benchmark datasets demonstrate the superiority and rationality of AMIFN.

ASKAT: Aspect Sentiment Knowledge Graph Attention Network for Recommendation

In modern online life, recommender systems can help us filter unimportant information. Researchers of recommendation algorithms usually utilize historical interaction data to mine potential user preferences. However, most existing methods use rating data to mine user interest preferences, ignoring rich textual information such as reviews. Although some researchers have attempted to combine ratings and reviews for recommendation, we believe the following shortcomings still exist. First, existing methods are overly dependent on the accuracy of external sentiment analysis tools. Second, existing methods do not fully utilize the features extracted from reviews. Further, existing methods focus only on the aspects that users like, while ignoring the aspects that users dislike, and they cannot completely model users’ true preferences. To address the above issues, in this paper, we propose a recommendation model based on the aspect of the sentiment knowledge graph attention network (ASKAT). We first use the improved aspect-based sentiment analysis algorithm to extract aspectual sentiment features from reviews. Then, to overcome the difficulty in underutilizing the information extracted from the comments, we build aspects of sentiment-enhanced collaborative knowledge mapping. After that, we propose a new graph attention network that uses sentiment-aware attention mechanisms to aggregate neighbour information. Finally, our experimental results on three datasets, Movie, Amazon book, and Yelp, show that our model consistently outperforms the baseline model in two recommendation scenarios, click-through-rate prediction and Top-k recommendation. Compared with other models, the method shows significant improvement in both recommendation accuracy and personalised recommendation effectiveness.

Optimized neural attention mechanism for aspect-based sentiment analysis framework with optimal polarity-based weighted features

Optimized neural attention mechanism for aspect-based sentiment analysis framework with optimal polarity-based weighted features