Aspect Sentiment Classification Research Articles

Optimizing Aspect Term Extraction and Sentiment Classification through Attention Mechanism and Sparse Attention Techniques

Optimizing Aspect Term Extraction and Sentiment Classification through Attention Mechanism and Sparse Attention Techniques

A hybrid method for enhancing aspect sentiment classification in the french language: combining rule-based and learning methods

A hybrid method for enhancing aspect sentiment classification in the french language: combining rule-based and learning methods

DEEP LEARNING APPROACH FOR ASPECT CATEGORY DETECTION: A BIBLIOMETRIC ANALYSIS

This article presents a quantitative and qualitative assessment of current research trends by conducting a bibliometric analysis of the sentiment analysis literature from 2020 to March 2024 using the Scopus database. Our focus is on the review of scientific documents, the arrangement of subject categories, the research trend in aspect category detection, the top 10 scholars that write the most number articles in aspect category detection and keyword trends. Our research shows that specialists in computer science, engineering, mathematics, medicine, decision science, material science, social sciences, business and management accounting, energy, and health are the most common topic groups in this industry. A study of keywords shows that terms like “BERT” and “deep learning” are frequently used together. This highlights the use of sophisticated models like BERT in this field and suggests a tendency to use innovative architecture to achieve better results. Conversely, although terms such as “sentiment analysis” and “aspect-based sentiment analysis” have modest frequency, their link strengths indicate a significant correlation with the main theme, emphasising the relationship between aspect category detection and sentiment analysis in research projects. we also provide the deep-learning technique used by the author for aspect category detection.

Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis

Urdu, with its rich linguistic complexity, poses significant challenges for computational sentiment analysis. This study presents an enhanced version of UrduAspectNet, specifically designed for Aspect-Based Sentiment Analysis (ABSA) in Urdu. We introduce key innovations including the incorporation of Biaffine Attention into the model architecture, which synergizes XLM-R embeddings, a bidirectional LSTM (BiLSTM), and dual Graph Convolutional Networks (GCNs). Additionally, we utilize dependency parsing to create the adjacency matrix for the GCNs, capturing syntactic dependencies to enhance relational representation. The improved model, termed Enhanced UrduAspectNet, integrates POS and lemma embeddings, processed through BiLSTM and GCN layers, with Biaffine Attention enhancing the extraction of intricate aspect and sentiment relationships. We also introduce the use of BIO tags for aspect term identification, improving the granularity of aspect extraction. Experimental results demonstrate significant improvements in both aspect extraction and sentiment classification accuracy. This research advances Urdu sentiment analysis and sets a precedent for leveraging sophisticated NLP techniques in underrepresented languages.

Exploring large language models for the generation of synthetic training samples for aspect-based sentiment analysis in low resource settings

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in sentiment analysis, aiming to identify sentiment expressed towards specific aspects of an entity. This paper explores the use of Large Language Models (LLMs), specifically GPT-3.5-turbo and Llama-3-70B, for generating annotated data in Aspect-Based Sentiment Analysis (ABSA), aiming to address the scarcity of labeled datasets in the field. Two low-resource scenarios are considered, with 25 and 500 manually annotated examples available. In the 25-example scenario, adding synthetic examples generated through few-shot prompting resulted in F1 scores of 81.33 for Aspect Category Detection (ACD) and 71.71 for Aspect Category Sentiment Analysis (ACSA). For the 500-example scenario, synthetic data augmentation showed a notable gain only for the ACSA task, raising the F1 score from 84.54 to 86.70.

Aspect-based sentiment analysis: A dual-task learning architecture using imbalanced maximized-area under the curve proximate support vector machine and reinforcement learning

Aspect-based sentiment analysis (ABSA) deals with understanding sentiments about specific attributes. Aspect term extraction (ATE) plays an essential role in aspect sentiment classification (ASC) by specifying the main aspects for sentiment accuracy. This paper introduces a dual-task framework for ATE and ASC models in which, the ATE model uses the imbalanced maximized-area under the curve proximate support vector machine (ImAUC-PSVM) method to mitigate the imbalance problem and the output of ATE serves as the reward in the training process of ASC supported by RL (reinforcement learning). The ImAUC-PSVM uses the conventional PSVM (proximate support vector machine) strength for sentiment tasks and the AUC (area under the curve) optimization for imbalance addressing. The ASC model employs multiple kernels for word embedding, with a transductive long short-term memory (TLSTM) for sentence representation that considers samples near the test point for better model refinement. The policy of the RL-based training strategy of the ASC model is represented by a multilayer perceptron (MLP) that categorizes sentences into positive, negative and neutral sentiments. In this RL context, aspect terms serve as rewards in the training process. The scope loss function (SLF) is also integrated within RL to rectify dataset imbalances. To optimize ATE and ASC models, an artificial bee colony (ABC) technique is incorporated for hyperparameter optimization. Experiments and evaluations conducted on English (Restaurant and Laptop) and Hindi datasets show the superiority of the proposed model over existing models of the literature. Moreover, transfer learning (TL) is applied to a Twitter dataset to assess its effect on ATE. The F-measures of the ATE model are obtained as 90.12 and 84.10 and the accuracies of the ASC model are calculated as 84.10 and 82.02 on Restaurant and Laptop datasets, respectively.

EAFL: Equilibrium Augmentation Mechanism to Enhance Federated Learning for Aspect Category Sentiment Analysis

Aspect Category Sentiment Analysis (ACSA) involves identifying sentiment categories for specific aspects of a sentence. Despite the progress made in pre-trained language models for extracting semantic and categorical representations, the sharing of sensitive data remains challenging due to data privacy protection. Federated Learning (FL) offers a solution by enabling decentralized model training without accessing raw datasets. However, isolated data encounters difficulties with text heterogeneity, which is crucial for aspect-based sentiment analysis (ABSA) subtask like ACSA. To address these problems, this paper presents a novel method called the Equilibrium Augmentation Mechanism to Enhance Federated Learning for Aspect Category Sentiment Analysis (EAFL-ACSA). The EAFL mechanism seamlessly integrates FL’s data privacy preservation capabilities with a variety of augmented data representations. Our unified model safeguards data privacy through FL and incorporates multiple features through data augmentation to enhance encrypted data. Furthermore, the parameter-efficient tuning mechanism incorporated in our framework enhances model performance and generates fine-tuned augmented sentences. Extensive experiments on five benchmark datasets demonstrates that EAFL consistently outperforms the best models, achieving a competitive level of performance while protecting data privacy. The equilibrium data augmentation mechanism not only advances aspect category detection but also enhances semantic data representations, effectively addressing critical concerns surrounding data privacy preservation within a unified framework.

Neural multi-task learning for end-to-end Arabic aspect-based sentiment analysis

Most existing aspect-based sentiment analysis (ABSA) methods perform the tasks of aspect extraction and sentiment classification independently, assuming that the aspect terms are already determined when handling the aspect sentiment classification task. However, such settings are neither practical nor appropriate in real-life applications, as aspects must be extracted prior to sentiment classification. This study aims to overcome this shortcoming by jointly identifying aspect terms and the corresponding sentiments using a multi-task learning approach based on a unified tagging scheme. The proposed model uses the Bidirectional Encoder Representations from Transformers (BERT) model to produce the input representations, followed by a Bidirectional Gated Recurrent Unit (BiGRU) layer for further contextual and semantic coding. An attention layer is added on top of BiGRU to force the model to focus on the important parts of the sentence. Finally, a Conditional Random Fields (CRF) layer is used to handle inter-label dependencies. Experiments conducted on a reference Arabic hotel dataset show that the proposed model significantly outperforms the baseline and related work models.

Contrastive Learning for Boosting Knowledge Transfer in Task-Incremental Continual Learning of Aspect Sentiment Classification Tasks

Abstract: Continual learning (CL) aims to learn a sequence of tasks, with task datasets emergingincrementally over time, and without a predetermined number of tasks. CL models strive to achievetwo primary objectives: preventing catastrophic forgetting and facilitating knowledge transfer between tasks. Catastrophic forgetting refers to the sharp decline in the performance of CL modelson previously learned tasks as new ones are learned. Knowledge transfer, which leverages acquiredknowledge from previous tasks, empowers the CL model to adeptly tackle new tasks. However, onlya few CL models proposed by far successfully achieve those two objectives simultaneously. In thispaper, we present a task-incremental CL based model that leverages a pre-trained language model(i.e., BERT) with injected CL-plugins to mitigate catastrophic forgetting in continual learning. Additionally, we propose the utilization of two contrastive learning-based losses, namely contrastiveensemble distillation (CED) and contrastive supervised learning of the current task (CSC) losses,to enhance our model’s performance. The CED loss improves the knowledge transferability of ourcontinual learning model, while the CSC loss enhances its performance for the current learning task.Experimental results on benchmark datasets demonstrate that our proposed model outperforms allexisting continual learning models in the task-incremental learning setting for continual aspect sentiment classification.Keywords: Continual Learning, Contrastive Learning, Aspect-Sentiment Classification.

Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models

The rapid rise of e-commerce platforms has changed people's shopping habits, driving the popularity of online shopping. Users express their opinions on products and services by purchasing products on platforms and posting comments. These comment data contain rich user experiences, which are crucial for enterprises to understand user needs and improve product quality. Sentiment analysis of comment text is an important research direction in text mining, focusing on how to extract user evaluations of products from comment data to provide comprehensive, authentic, and accurate product feedback. This paper mainly investigates aspect-based fine-grained sentiment analysis. Because users express multiple sentiments towards different aspects in comments, coarse-grained sentiment analysis cannot accurately capture users' sentiment tendencies. This study utilizes artificial intelligence technology based on deep learning, firstly, it constructs auxiliary training samples to transform aspect sentiment tasks into machine reading comprehension or language inference tasks, using BERT model to extract text features and sentence features from comment data. To guide the model to focus on the features most relevant to the given aspect, a cross-attention mechanism is utilized to cross-focus the features of text with aspect category features. Finally, the sentiment polarity of given aspect category text can be predicted through a forward network. Experimental results on multiple datasets demonstrate that this method outperforms other deep learning models.

Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models

The rapid rise of e-commerce platforms has changed people's shopping habits, driving the popularity of online shopping. Users express their opinions on products and services by purchasing products on platforms and posting comments. These comment data contain rich user experiences, which are crucial for enterprises to understand user needs and improve product quality. Sentiment analysis of comment text is an important research direction in text mining, focusing on how to extract user evaluations of products from comment data to provide comprehensive, authentic, and accurate product feedback. This paper mainly investigates aspect-based fine-grained sentiment analysis. Because users express multiple sentiments towards different aspects in comments, coarse-grained sentiment analysis cannot accurately capture users' sentiment tendencies. This study utilizes artificial intelligence technology based on deep learning, firstly, it constructs auxiliary training samples to transform aspect sentiment tasks into machine reading comprehension or language inference tasks, using BERT model to extract text features and sentence features from comment data. To guide the model to focus on the features most relevant to the given aspect, a cross-attention mechanism is utilized to cross-focus the features of text with aspect category features. Finally, the sentiment polarity of given aspect category text can be predicted through a forward network. Experimental results on multiple datasets demonstrate that this method outperforms other deep learning models.

Aspect-Level Sentiment Analysis of Customer Reviews Based on Neural Multi-task Learning

In the era of big data, major e-commerce platforms are facing the challenge of an exponential growth in the number of user comments. Effectively utilizing these comments has become an urgent issue. Traditional manual statistical methods are no longer able to meet the demands for accuracy and real-time analysis. The rise of artificial intelligence-based text mining techniques provides a new approach to address this problem. By building deep learning analysis models, it is possible to uncover user preferences and product characteristics, helping businesses proactively adjust sales strategies, improve product and service quality, and achieve precise marketing. Text sentiment analysis is one of the important research directions in the field of text mining. Its basic idea is to transform subjective texts with emotional color into structured data, and then use machine learning, deep learning, and other artificial intelligence technologies to extract emotional features and discover knowledge patterns. This article proposes a deep learning-based sentiment analysis model, which includes a shared sentiment prediction layer used to transfer emotional knowledge between different aspect categories and alleviate the problem of insufficient data. The model consists of two parts: the Aspect Category Detection (ACD) classifier based on attention mechanism and the Aspect Category Sentiment Analysis (ACSA) classifier. The ACD part generates word weights using attention mechanism, while the ACSA part predicts the sentiment of words and combines weights to determine the sentiment of aspect categories within sentences.

MACSA: A multimodal aspect-category sentiment analysis dataset with multimodal fine-grained aligned annotations

MACSA: A multimodal aspect-category sentiment analysis dataset with multimodal fine-grained aligned annotations

A novel self-supervised contrastive learning based sentence-level attribute induction method for online satisfaction evaluation

Satisfaction evaluation provides valuable support for procurement decisions and product improvement on e-commerce platforms. As an important task of satisfaction evaluation, attribute induction is essential to identify relevant aspects and demands of user-generated evaluations. However, previous methods have often directly predicted typical attributes using individual words, neglecting valuable sentence-level information that could significantly enhance effectiveness. To alleviate this issue, this article introduces the concept of representations of potential attributes (PARs) to explicitly capture sentence-level information regarding product attributes in customer reviews. It further develops a novel self-supervised contrastive learning based sentence-level attribute induction method (SSCL-SAI) to train these PARs. Recognizing that sentences are the basic units of human expression, this approach mitigates information loss. Then, an aspect-category sentiment analysis model is used to transform the unstructured online textual reviews into linguistic 2-tuple for obtaining final satisfaction evaluation result. These 2-tuples provide a representation of sentiment orientations on a continuous numerical scale, enabling precise linguistic operations. Afterwards, an illustrative example conducted on real-world laptop product satisfaction analysis testify the validity of the approach. Finally, the comparative analysis shows the effectiveness of the proposed SSCL-SAI, and experiments on the impact of hyperparameter settings demonstrate that SSCL-SAI can be applied in various scenarios.

CAT: Continual Adapter Tuning for aspect sentiment classification

Humans can continually acquire, improve, and transfer knowledge throughout their lifespan so that they can accurately identify sentiment polarities of the data attributed to different domains. However, the continual learning of incrementally available aspect sentiment classification (ASC) tasks from different domains with non-stationary data distributions remains a long-standing challenge for learning-based models due to the catastrophic forgetting problem, which is the tendency to completely and abruptly forget previously learned knowledge upon learning new knowledge. In this work, we present Continual Adapter Tuning (CAT), a parameter-efficient framework that not only avoids catastrophic forgetting but also enables knowledge transfer from learned ASC tasks to new ASC tasks. To avoid catastrophic forgetting, we only learn and store a task-specific adapter for each ASC task while freezing the backbone pre-trained model. To promote new task learning, we propose a continual adapter initialization technique to transfer knowledge from preceding tasks. Besides, we also develop a novel label-aware contrastive learning to simultaneously learn the features of input samples and the parameters of classifiers in the same space so that we can efficiently classify a sample with the help of label semantics. To eliminate the need for task IDs in testing, we propose a simple yet efficient majority sentiment polarity voting strategy to obtain final sentiment polarities according to the polarities predicted by all reasoning paths in the adapter architecture. Experimental results show the high effectiveness of our CAT by achieving new state-of-the-art performance.

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.

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.

A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis

Sentiment Analysis is a useful tool in several social and business contexts. Aspect Sentiment Classification is a subtask in Sentiment Analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different Deep Learning models have been proposed to solve Aspect Sentiment Classification focus on a specific domain such as restaurant,hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The Continual Learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoid low aspect classification performance in Continual Learning is challenging. As a consequence, potential neural networkweight shifts in the learning process in different domains or datasets.In this paper, a novel Aspect Sentiment Classification approach is proposed. Our approach combines a Transformer Deep Learning technique with a Continual Learning algorithm in different domains. The input layer used is the pre‐trained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 .

A Model of Continual and Deep Learning for Aspect Based in Sentiment Analysis

Sentiment Analysis is a useful tool in several social and business contexts. Aspect Sentiment Classification is a subtask in Sentiment Analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different Deep Learning models have been proposed to solve Aspect Sentiment Classification focus on a specific domain such as restaurant,hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The Continual Learning approach with neural networks has been used to solve aspect classification in multiple domains. However, avoid low aspect classification performance in Continual Learning is challenging. As a consequence, potential neural networkweight shifts in the learning process in different domains or datasets.In this paper, a novel Aspect Sentiment Classification approach is proposed. Our approach combines a Transformer Deep Learning technique with a Continual Learning algorithm in different domains. The input layer used is the pre‐trained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 .

Knowledge-enhanced graph convolutional networks for Arabic aspect sentiment classification

Knowledge-enhanced graph convolutional networks for Arabic aspect sentiment classification