Aspect Category Detection Research Articles

Aspect Category Detection Using Bi-LSTM-Based Deep Learning for Sentiment Analysis for Hindi

Aspect Category Detection Using Bi-LSTM-Based Deep Learning for Sentiment Analysis for Hindi

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.

Enhancing aspect category detection in imbalanced online reviews: An integrated approach using Select-SMOTE and LightGBM

Aspect category detection (ACD) is a pivotal subtask within the field of sentiment analysis in natural language processing, aiming to identify implicit aspect category information in online review texts. In real-world scenarios of online review category detection tasks, data imbalance often arises, leading to skewed distributions among distinct review categories. This phenomenon poses substantial challenges for accurately recognizing minority categories through modeling. To address this, we propose a method for detecting imbalanced aspect categories by combining the selective synthetic over-sampling (Select-SMOTE) algorithm with the light gradient boosting machine (LightGBM). Our approach commences with text data representation through features, followed by a strategy involving joint sample partitioning and boundary optimization within the feature space to generate minority class samples. This partitioning strategy aligns generated data more closely with the original distribution, while the boundary optimization module enhances classification performance by eliminating samples near boundaries. Subsequently, the balanced dataset is input to the LGB model, enabling the extraction of aspect category information through parameter optimization and class weight assignment. Finally, our method is evaluated using the SemEval and SentiHood datasets and compared with prevailing sampling methods and classification models. Empirical results manifestly demonstrate the method’s superiority across diverse metrics, reflecting robustness and effective mitigation of imbalanced data challenges in ACD.

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.

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.

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.

MTL-AraBERT: An Enhanced Multi-Task Learning Model for Arabic Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis; it works on an aspect level. It mainly focuses on extracting aspect terms from text or reviews, categorizing the aspect terms, and classifying the sentiment polarities toward each aspect term and aspect category. Aspect term extraction (ATE) and aspect category detection (ACD) are interdependent and closely associated tasks. However, the majority of the current literature on Arabic aspect-based sentiment analysis (ABSA) deals with these tasks individually, assumes that aspect terms are already identified, or employs a pipeline model. Pipeline solutions employ single models for each task, where the output of the ATE model is utilized as the input for the ACD model. This sequential process can lead to the propagation of errors across different stages, as the performance of the ACD model is influenced by any errors produced by the ATE model. Therefore, the primary objective of this study was to investigate a multi-task learning approach based on transfer learning and transformers. We propose a multi-task learning model (MTL) that utilizes the pre-trained language model (AraBERT), namely, the MTL-AraBERT model, for extracting Arabic aspect terms and aspect categories simultaneously. Specifically, we focused on training a single model that simultaneously and jointly addressed both subtasks. Moreover, this paper also proposes a model integrating AraBERT, single pair classification, and BiLSTM/BiGRU that can be applied to aspect term polarity classification (APC) and aspect category polarity classification (ACPC). All proposed models were evaluated using the SemEval-2016 annotated dataset for the Arabic hotel dataset. The experiment results of the MTL model demonstrate that the proposed models achieved comparable or better performance than state-of-the-art works (F1-scores of 80.32% for the ATE and 68.21% for the ACD). The proposed SPC-BERT model demonstrated high accuracy, reaching 89.02% and 89.36 for APC and ACPC, respectively. These improvements hold significant potential for future research in Arabic ABSA.

Variational Hybrid-Attention Framework for Multi-Label Few-Shot Aspect Category Detection

Multi-label few-shot aspect category detection (FS-ACD) is a challenging sentiment analysis task, which aims to learn a multi-label learning paradigm with limited training data. The difficulty of this task is how to use limited data to generalize effective discriminative representations for different categories. Nowadays, all advanced FS-ACD works utilize the prototypical network to learn label prototypes to represent different aspects. However, such point-based estimation methods are inherently noise-susceptible and bias-vulnerable. To this end, this paper proposes a novel Variational Hybrid-Attention Framework (VHAF) for the FS-ACD task. Specifically, to alleviate the data noise, we adopt a hybrid-attention mechanism to generate more discriminative aspect-specific embeddings. Then, based on these embeddings, we introduce the variational distribution inference to obtain the aspect-specific distribution as a more robust aspect representation, which can eliminate the scarce data bias for better inference. Moreover, we further leverage an adaptive threshold estimation to help VHAF better identify multiple relevant aspects. Extensive experiments on three datasets demonstrate the effectiveness of our VHAF over other state-of-the-art methods. Code is available at https://github.com/chengzju/VHAF.

Auto-verbalizer filtering for prompt-based aspect category detection

Aspect category detection (ACD) is a basic task in sentiment analysis that aims to identify the specific aspect categories discussed in reviews. In the case of limited label resources, prompt-based models have shown promise in few-shot ACD. However, their current limitations lie in the manual selection or reliance on external knowledge for obtaining the verbalizer, a critical component of prompt learning that maps predicted words to final categories. To solve these issues, we propose an ACD method to automatically build the verbalizer in prompt learning. Our approach leverages the semantic expansion of category labels as prompts to automatically acquire initial verbalizer tokens. Additionally, we introduce an indicator mechanism for auto-verbalizer filtering to obtain reasonable verbalizer words and improve the predicting aspect category reliability of the method. In zero-shot task, our model exhibits an average performance improvement of 7.5% over the second-best model across four ACD datasets. For the other three few-shot tasks, the average performance improvement over the second-best model is approximately 2%. Notably, our method demonstrates effectiveness, particularly in handling general or miscellaneous category aspects.

Aspect Category Detection of Mobile Edge Customer Reviews: A Distributed and Trustworthy Restaurant Recommendation System

Aspect Category Detection of Mobile Edge Customer Reviews: A Distributed and Trustworthy Restaurant Recommendation System

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.

Enhancing Aspect Category Detection Through Hybridised Contextualised Neural Language Models: A Case Study In Multi-Label Text Classification

Abstract Recently, the field of Natural Language Processing (NLP) has made significant progress with the evolution of Contextualised Neural Language Models (CNLMs) and the emergence of large LMs. Traditional and static language models exhibit limitations in tasks demanding contextual comprehension due to their reliance on fixed representations. CNLMs such as BERT and Semantic Folding aim to produce feature-rich representations by considering a broader linguistic context. In this paper, Deep Learning-based Aspect Category Detection approaches are introduced to perform text classification. The study extensively assesses classification model performance, emphasising enhanced representativeness and optimised feature extraction resolution using CNLMs and their hybridised variants. The effectiveness of the proposed approaches is evaluated on benchmark datasets of 4500 reviews from the laptop and restaurant domains. The results show that the proposed approaches using hybridised CNLMs outperform state-of-the-art methods with an f-score of 0.85 for the laptop and f-scores higher than 0.90 for the restaurant dataset. This study represents a pioneering work as one of the initial research efforts aiming to jointly evaluate the representation performance of CNLMs with different architectures to determine their classification capabilities. The findings indicate that the proposed approaches can enable the development of more effective classification models in various NLP tasks.

Enhanced approach of multilabel learning for the Arabic aspect category detection of the hotel reviews

AbstractIn many fields, like aspect category detection (ACD) in aspect‐based sentiment analysis, it is necessary to label each instance with more than one label at the same time. This study tackles the multilabel classification problem in the ACD task for the Arabic language. For this purpose, we used Arabic hotel reviews from the SemEval‐2016 dataset, comprising 13,113 annotated tuples provided for training (10,509) and testing (2,604). To extract valuable information, we first propose specific data preprocessing. Then, we suggest using the dynamic weighted loss function and a data augmentation method to fix the problem with this dataset's imbalance. Using two possible approaches, we develop new ways to find different categories of things in a review sentence. The first is based on classifier chains using machine learning models. The second is based on transfer learning using pretrained AraBERT fine‐tuning for contextual representation. Our findings show that both approaches outperformed the related works for ACD on the Arabic SemEval‐2016. Moreover, we observed that AraBERT fine‐tuning performed much better and achieved a promising ‐score of .

Hybrid Regularizations for Multi-Aspect Category Sentiment Analysis

Aspect level sentiment classification aims to identify the sentiment polarity towards a particular aspect in a sentence. Previous attention-based methods generate an aspect-specific representation for each aspect and employ it to classify the sentiment polarity. However, normalized attention scores scatter over every word in the sentence, resulting in two issues. First, the attention may inherently introduce noise and downgrade the performance. Second, the opinion words may be “diluted” by other words, while the opinion feature should dominate for sentiment analysis. The issues become more severe in multi-aspect sentences. In this paper, we address the above two issues via hybrid regularizations, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aspect-level</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">task-level regularizations</i> . Concretely, the aspect-level regularizations constrain the attention weights to alleviate noise. Among them, orthogonal regularization is designed for multi-aspect sentences and sparse regularization is for single-aspect sentences. To extract sentiment-dominant features, task-level regularization is proposed by introducing an orthogonal auxiliary task, i.e., aspect category detection. This regularization can allocate task-oriented context information for specific downstream tasks. Extensive experimental results on three public datasets demonstrate the effectiveness of the proposed approach in both single-task and multi-task scenarios.

Aspect-Based Sentiment Analysis for Arabic Food Delivery Reviews

Business customers and consumers share their reviews online on social platforms such as Twitter. Therefore, Twitter data sentiment analysis is extremely useful for both research and commercial purposes. Manually analyzing reviews takes a long time and effort, hence, automatic sentiment analysis is required. In this article, we address aspect-based sentiment analysis for Arabic food delivery reviews using several deep learning approaches. In particular, we propose to use Transformer-based models (GigaBERT and AraBERT), Bi-LSTM-CRF, and LSTM, as well as a classical machine learning algorithm (SVM). We also present our dataset of food delivery service reviews, which we collected from Twitter. We annotated them and used them for training and evaluating our approaches. The experiments show that both GigaBERT and AraBERT outperformed the other models in all the tasks. The Transformer-based models received F1-scores of 77% in the aspect terms detection task, 82% in the Aspect category detection task, and 81% in the aspect polarity detection task, gaining 2%, 4%, and 4% over Bi-LSTM-CRF and LSTM in the first, second, and third tasks, respectively.

A semi supervised approach to Arabic aspect category detection using Bert and teacher-student model.

Aspect-based sentiment analysis tasks are well researched in English. However, we find such research lacking in the context of the Arabic language, especially with reference to aspect category detection. Most of this research is focusing on supervised machine learning methods that require the use of large, labeled datasets. Therefore, the aim of this research is to implement a semi-supervised self-training approach which utilizes a noisy student framework to enhance the capability of a deep learning model, AraBERT v02. The objective is to perform aspect category detection on both the SemEval 2016 hotel review dataset and the Hotel Arabic-Reviews Dataset (HARD) 2016. The four-step framework firstly entails developing a teacher model that is trained on the aspect categories of the SemEval 2016 labeled dataset. Secondly, it generates pseudo labels for the unlabeled HARD dataset based on the teacher model. Thirdly, it creates a noisy student model that is trained on the combined datasets (∼1 million sentences). The aim is to minimize the combined cross entropy loss. Fourthly, an ensembling of both teacher and student models is carried out to enhance the performance of AraBERT. Findings indicate that the ensembled teacher-student model demonstrates a 0.3% improvement in its micro F1 over the initial noisy student implementation, both in predicting the Aspect Categories in the combined datasets. However, it has achieved a 1% increase over the micro F1 of the teacher model. These results outperform both baselines and other deep learning models discussed in the related literature.

Graph Enhanced Transformer for Aspect Category Detection

Graph Enhanced Transformer for Aspect Category Detection

Semi-Supervised Model for Aspect Sentiment Detection

Advancements in text representation have produced many deep language models (LMs), such as Word2Vec and recurrent-based LMs. However, there are scarce works that focus on detecting implicit sentiments with a small amount of labelled data because there are many different review areas. Deep learning techniques are suitable to automate the representation learning process. Hence, we proposed a semi-supervised aspect-based sentiment analysis (ABSA) model for online review to predict explicit and implicit sentiment in three domains (laptop, restaurant, and hotel). The datasets of this study, S1 and S2, were obtained from a standard SemEval online competition and Amazon review datasets. The proposed models outperform the previous baseline models regarding the F1-score of aspect category detection and accuracy of sentiment detection. This study finds more relevant aspects and accurate sentiment for ABSA by developing more stable and robust models. The accuracy of sentiment detection is 84.87% in the restaurant domain on the first dataset. For the second dataset, the proposed method achieved 84.43% in the laptop domain, 85.21% in the restaurant domain, and 85.57% in the hotel domain. The novelty is the proposed new semi-supervised model for aspect sentiment detection with embedded aspect inspired by the encoder–decoder architecture in the neural machine translation (NMT) model.

Combining BERT with TCN-BiGRU for enhancing Arabic aspect category detection

Aspect-based sentiment analysis (ABSA) is a challenging task of sentiment analysis that aims at extracting the discussed aspects and identifying the sentiment corresponding to each aspect. We can distinguish three main ABSA tasks: aspect term extraction, aspect category detection (ACD), and aspect sentiment classification. Most Arabic ABSA research has relied on rule-based or machine learning-based methods, with little attention to deep learning techniques. Moreover, most existing Arabic deep learning models are initialized using context-free word embedding models, which cannot handle polysemy. Therefore, this paper aims at overcoming the limitations mentioned above by exploiting the contextualized embeddings from pre-trained language models, specifically the BERT model. Besides, we combine BERT with a temporal convolutional network and a bidirectional gated recurrent unit network in order to enhance the extracted semantic and contextual features. The evaluation results show that the proposed method has outperformed the baseline and other models by achieving an F1-score of 84.58% for the Arabic ACD task. Furthermore, a set of methods are examined to handle the class imbalance in the used dataset. Data augmentation based on back-translation has shown its effectiveness through enhancing the first results by an overall improvement of more than 3% in terms of F1-score.

A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text