Aspect-based Sentiment Analysis Research Articles

AWA-GCN: Enhancing Chinese sentiment analysis with a novel GCN model for Triplet and Quadruplet Extraction at SIGHAN 2024 dimABSA Task

This study presents the AWA-GCN (Attention-Weighted Affine with Graph Convolutional Networks) model, a new method designed exclusively for the SIGHAN 2024 Chinese Dimensional Aspect-Based Sentiment Analysis (dimABSA) Task. This test requires participants to identify sentiment triples and sentiment quadruples from textual data. The data comprises aspects, sentiment views, categories, and intensities across valence-arousal dimensions. Our model differs from existing models by utilizing a complex multi-layer attention mechanism within a GCN architecture, instead of relying on pipeline techniques or Grid Tagging Schemes (GTS). This design successfully captures the intricate interconnections among many sentiment components. The AWA-GCN model is very innovative in its ability to effectively handle quadruple extraction, which is a complex problem that has not been handled by traditional methods. Our approach enhances the understanding of aspect-sentiment interactions by including sophisticated techniques like word-level attention and semantic graph representations. The AWA- GCN model outperforms previous baselines in precision, recall, and F1 score on the dimABSA dataset, as demonstrated by empirical assessments. Additionally, the model exhibits significant enhancements in capturing the dimensional features of sentiment expressions. The results validate the models exceptional ability to handle the complex nuances necessary for successful sentiment analysis in simple Chinese.

Aspect-based sentiment analysis via dual residual networks with sentiment knowledge

Aspect-based sentiment analysis via dual residual networks with sentiment knowledge

Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools

Mobile app reviews are valuable for gaining user feedback on features, usability, and areas for improvement. Analyzing these reviews manually is difficult due to volume and structure, leading to the need for automated techniques. This mapping study categorizes existing approaches for automated and semi-automated tools by analyzing 180 primary studies. Techniques include topic modeling, collocation finding, association rule-based, aspect-based sentiment analysis, frequency-based, word vector-based, and hybrid approaches. The study compares various tools for analyzing mobile app reviews based on performance, scalability, and user-friendliness. Tools like KEFE, MERIT, DIVER, SAFER, SIRA, T-FEX, RE-BERT, and AOBTM outperformed baseline tools like IDEA and SAFE in identifying emerging issues and extracting relevant information. The study also discusses limitations such as manual intervention, linguistic complexities, scalability issues, and interpretability challenges in incorporating user feedback. Overall, this mapping study outlines the current state of feature extraction from app reviews, suggesting future research and innovation opportunities for extracting software requirements from mobile app reviews, thereby improving mobile app development.

Data augmentation framework with enhanced graph convolutional network for cross-domain aspect-based sentiment analysis

Data augmentation framework with enhanced graph convolutional network for cross-domain aspect-based sentiment analysis

Polarity-aware deep attention network for aspect-based sentiment analysis

Polarity-aware deep attention network for aspect-based sentiment analysis

MuSe-CarASTE: A comprehensive dataset for aspect sentiment triplet extraction in automotive review videos

In the Aspect-Based Sentiment Analysis (ABSA) domain, the Aspect Sentiment Triplet Extraction (ASTE) task has emerged as a pivotal endeavor, offering insights into nuanced aspects, opinions, and sentiment relationships. This paper introduces “MuSe-CarASTE”, an extensive and meticulously curated dataset purpose-built to propel ASTE advancements within the automotive domain. The core emphasis of MuSe-CarASTE is on aspect, opinion, and sentiment triplets, facilitating a comprehensive analysis of product reviews. Comprising transcripts from MuSe-Car’s automotive video reviews, MuSe-CarASTE presents a sub-stantial collection of nearly 28,295 sentences organized into 5,500 segments. Each segment is meticulously annotated with multiple aspects, opinions, and sentiment labels, offering unprecedented granularity for ASTE tasks. The percentage agreement between annotated triples by different annotators over the randomly sampled subset of the dataset is 79.74 %, at similarity threshold τ = 0.60. We also experimented with four baseline models on our datset and report results. The distinctiveness of the dataset emerges from its extension into the automotive domain, shedding light on sentiment dynamics specific to vehicles. With the fusion of extensive content and real-world applicability, MuSe-CarASTE presents a fertile ground for Natural Language Processing (NLP) innovation. Researchers, practitioners, and data scientists can harness MuSe-CarASTE to build and evaluate NLP models tailored for challenges in ASTE. These challenges encompass intricate aspect-opinion relationships, multi-word aspect and opinion extraction, and the subtleties of vague language. Moreover, including aspects not verbatim in sentences introduces a practical dimension to our dataset, enabling real-world applications like review pattern analysis, summarization, and recommender system enhancement. As a pioneering benchmark for NLP model evaluation in ABSA, MuSe-CarASTE integrates content richness, real-world context, and sentiment complexity. The integration empowers the development of accurate, adaptable, and insightful sentiment analysis models within the automotive review landscape.

Aspect Based Sentiment Analysis Using Modified Latent Dirichlet Allocation and Optimized BERT with LSTM Classification

Aspect Based Sentiment Analysis Using Modified Latent Dirichlet Allocation and Optimized BERT with LSTM Classification

Aspect-Based Sentiment Analysis for Financial Review with Implicit Aspect and Opinion Using Semantic Similarity and Hybrid Approach

Aspect-Based Sentiment Analysis for Financial Review with Implicit Aspect and Opinion Using Semantic Similarity and Hybrid Approach

Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

Hierarchical attention for aspect extraction using LSTM in fine-grained sentiment analysis and evaluation

In the current digital world, aspect-based sentiment analysis (ABSA) from a review sentence is a big challenge. In recent works, the aspect term detection from the reviews of the discourse is performed independently, even for contextually interdependent reviews. Further, the baseline techniques could not correlate valid multi-word aspect extraction from different review sentences, which further degrades the aspect extraction performance. Therefore, this paper aims to improve the performance of the aspect term detection subtask using a supervised hierarchical attention-based model. A sentence alignment step is added as preprocessing before ABSA on multi-sentence reviews. This step removes deficient knowledge, captures aspect alignment from different review sentences, and properly relates the aspect to the context domain. Next, the context information is used to extract contextual aspects using word-vector representation. The input sentences and labeled data using sequence tagging are supplied to the proposed supervised hierarchical attention network. The proposed model can capture the semantics of words for different domains, enhancing the extraction of domain-specific relevant aspect terms. The experimental results shown in the SemEval-16 reviews present the effectiveness of the proposed attention-based approach for aspect term detection. The F-score is improved by approximately 3 % as compared to the recent hybrid unsupervised approach for both the laptop and restaurant domains when coreference resolution is not performed. On the other side, F-score is improved by approximately 4 % and 3 % as compared to the recent hybrid unsupervised approach for the laptop and restaurant domains, respectively, when coreference resolution is not performed. When the results of the proposed approach are compared with those of recent supervised approaches, the F-score of the proposed model is improved by 5 % and 9 % for laptops and restaurants, respectively, when coreference resolution is not performed. Whereas the F-score of the proposed model is improved by 9 % and 12 % for laptops and restaurants, respectively, when coreference resolution is performed before the aspect extraction subtask.

Aspect-aware semantic feature enhanced networks for multimodal aspect-based sentiment analysis

Aspect-aware semantic feature enhanced networks for multimodal aspect-based sentiment analysis

Enhancing aspect-based sentiment analysis through graph attention networks and supervised contrastive learning

Enhancing aspect-based sentiment analysis through graph attention networks and supervised contrastive learning

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.

EASE: An enhanced active learning framework for aspect-based sentiment analysis based on sample diversity and data augmentation

Aspect-Based Sentiment Analysis (ABSA) has received considerable attention in recent studies. Powerful pre-trained models were proposed which can be fine-tuned for many Natural Language Processing (NLP) tasks, including ABSA. However, fine-tuning these models needs a relatively large amount of labeled data. In this research, we propose EASE; an active learning framework to minimize manual labeling effort. We extend the active learning technique by incorporating the concept of sample diversity where similar samples are not selected for labeling. Furthermore, we maximize the utility of these samples by incorporating data augmentation. EASE was evaluated on three benchmark ABSA datasets from three different domains. The results show that the reduction of the number of needed labeled samples ranges from 88% to 94% among the three datasets while maintaining accuracy. Our results show that active learning is an effective approach to reduce manual labeling effort while maintaining comparable performance. Moreover, it is possible to reduce the number of labeled data even further by incorporating sample diversity and data augmentation while maintaining performance.

Dual syntax aware graph attention networks with prompt for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a challenging task due to the presence of multiple aspect words with different sentiment polarities in a sentence. Recently, pre-trained language models like BERT have been widely used as context encoders in ABSA. Graph neural networks have also been employed to extract syntactic and semantic information from sentence parsing trees, resulting in superior results. However, dependency trees may establish irrelevant dependencies for sentences with irregular syntax and complex structures. Additionally, previous methods have not fully utilized recent developments in pre-trained language models. Therefore, we propose a Dual Syntax aware Graph attention networks with Prompt (DSGP) model to address these issues. Our model utilizes prompt templates to maximize the potential of pre-trained models and masked vector outputs of templates as supplementary aspect feature representations. We also leverage both dependency trees and constituent trees with graph attention networks to extract different types of syntactic information. The dependency tree captures syntactic correlation between words, while the constituent tree provides a high-level formation of the sentence. Finally, the output from the prompt and parsing trees is fused and fed into a standard classifier. Experimental results on four public datasets demonstrate the competitive performance of our model.

Understanding Public Opinion towards ESG and Green Finance with the Use of Explainable Artificial Intelligence

This study leverages explainable artificial intelligence (XAI) techniques to analyze public sentiment towards Environmental, Social, and Governance (ESG) factors, climate change, and green finance. It does so by developing a novel multi-task learning framework combining aspect-based sentiment analysis, co-reference resolution, and contrastive learning to extract nuanced insights from a large corpus of social media data. Our approach integrates state-of-the-art models, including the SenticNet API, for sentiment analysis and implements multiple XAI methods such as LIME, SHAP, and Permutation Importance to enhance interpretability. Results reveal predominantly positive sentiment towards environmental topics, with notable variations across ESG categories. The contrastive learning visualization demonstrates clear sentiment clustering while highlighting areas of uncertainty. This research contributes to the field by providing an interpretable, trustworthy AI system for ESG sentiment analysis, offering valuable insights for policymakers and business stakeholders navigating the complex landscape of sustainable finance and climate action. The methodology proposed in this paper advances the current state of AI in ESG and green finance in several ways. By combining aspect-based sentiment analysis, co-reference resolution, and contrastive learning, our approach provides a more comprehensive understanding of public sentiment towards ESG factors than traditional methods. The integration of multiple XAI techniques (LIME, SHAP, and Permutation Importance) offers a transparent view of the subtlety of the model’s decision-making process, which is crucial for building trust in AI-driven ESG assessments. Our approach enables a more accurate representation of public opinion, essential for informed decision-making in sustainable finance. This paper paves the way for more transparent and explainable AI applications in critical domains like ESG.

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.

IFusionQuad: A novel framework for improved aspect-based sentiment quadruple analysis in dialogue contexts with advanced feature integration and contextual CloBlock

Aspect-based sentiment analysis (ABSA) represents a crucial field of natural language processing (NLP). It focuses on deriving detailed sentiment insights from textual content. Dialogue-level aspect-based sentiment quadruple extraction (DiaASQ) is specifically concerned with pinpointing target-aspect-opinion-emotion quadruples within conversations. DiaASQ is important in industries like e-commerce, social media analytics, and customer feedback. However, Current ABSA approaches predominantly focus on single-text scenarios, often overlooking the complexities involved in sentiment analysis within conversational contexts. To fill this gap, this paper presents the IFusionQuad model, which is specifically designed for the DiaASQ task. Our contributions include the innovative integration of CloBlock in ABSA, enhancing feature representation with context-aware weights. The InteractiveNet Fusion Module further advances dialogue understanding by aggregating dialogue-specific features such as threads, speakers, and replies. Components such as CloBlock, gating mechanism, and Biaffine attention effectively mitigate data noise issues, improving the relevance of feature extraction. Empirical evaluation on standard datasets demonstrates that the IFusionQuad model outperforms baseline methods, achieving substantial improvements in quadruple extraction. Specifically, our model shows a 6.59% increase in micro F1 and a 7.05% increase in identification F1 for Chinese datasets, and a 2.65% and 4.69% increase in micro F1 and identification F1, respectively, for English datasets. The results clearly demonstrate our IFusionQuad model’s efficacy, which consistently outperforms baseline models across all evaluation datasets on the DiaASQ task.

Aspect Based Sentiment Analysis Using Self-Attention Based LSTM Model with Word Embedding

Aspect Based Sentiment Analysis Using Self-Attention Based LSTM Model with Word Embedding

Dual edge-embedding graph convolutional network for unified aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) mainly involves three elements, i.e., aspect, target, and sentiment. Most existing research focuses on aspect-sentiment or target-sentiment separately, while connection among the three elements is still underexploited. In order to extract three interrelated elements from a sentence simultaneously, we propose a unified end-to-end model, which is based on one aspect via a virtual edge attention mechanism and a graph convolutional network (GCN) to detect related targets and to analyze the corresponding sentiment polarity. The virtual edges are decided by the association intensity between the aspect and the words of the sentence. Instead of naively regarding all connecting edges as binary values, we design a novel GCN with a dual edge-embedding method to help information flow among each word in the sentence and between the aspect category and the sentence. Extensive experiments on two open datasets show that our model outperforms the state-of-the-art models in the aspect-target-sentiment triples detection task and its two subtasks.