Aspect-level Sentiment Analysis Research Articles

Multi-granularity visual-textual jointly modeling for aspect-level multimodal sentiment analysis

Multi-granularity visual-textual jointly modeling for aspect-level multimodal sentiment analysis

Exploring gender stereotypes in financial reporting: An aspect-level sentiment analysis using big data and deep learning

This study delves into the intricate interplay between gender stereotypes and financial reporting through an aspect-level sentiment analysis approach. Leveraging Big Data comprising 129,251 human face images extracted from 2085 financial reports in Chile, and employing Deep Learning techniques, we uncover the underlying factors influencing the representation of women in financial reports. Our findings reveal that gender stereotypes, combined with external economic factors, significantly shape the portrayal of women in financial reports, overshadowing intentional efforts by companies to influence stakeholder perceptions of financial performance. Notably, economic expansion periods correlate with a decline in women's representation, while economic instability amplifies their portrayal. Furthermore, the financial inclusion of women positively correlates with their presence in financial report images. Our results underscore a bias in image selection within financial reports, diverging from the neutrality principles advocated by the International Accounting Standards Board (IASB). This pioneering study, combining Big Data and Deep Learning, contributes to gender stereotype literature, financial report soft information research, and business impression management research.

Discovery of smart hotels’ competitiveness based on online reviews

The rapid expansion of smart hotels in recent years has aroused researchers' interest in their competitiveness. While previous research has identified factors influencing the competitiveness of smart hotels, little effort has been devoted to understanding the specific competitive strengths and weaknesses of individual smart hotels. This study addresses the research gap by proposing a novel artificial intelligence framework that analyzes the competitive strengths and weaknesses of smart hotels based on online review data. The framework employs aspect-level sentiment analysis, outlying aspect mining, and theory of lodging to identify detailed competitive strengths and weaknesses of smart hotels from different perspectives. The effectiveness of the proposed framework is demonstrated through a case study of major smart hotels worldwide. The results extend the theory of lodging by incorporating new factors related to smart hotels and provide valuable insights that can help smart hotel practitioners develop strategies to enhance competitiveness at the firm, brand, and market levels.

Integrating Fractal Snow Ablation Optimizer with Bayesian Machine Learning for Aspect-Level Sentiment Analysis on Social Media

Integrating Fractal Snow Ablation Optimizer with Bayesian Machine Learning for Aspect-Level Sentiment Analysis on Social Media

Distance-reconstructed dependency enhanced aspect-based sentiment analysis with sentiment strength

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that requires the detection of sentiment polarity towards specific aspects. Dependency tree-based graph convolutional network models (GCN) have been widely applied in ABSA. These studies utilize syntactic analysis tools to derive syntactic dependency graphs, which are then inputted into GCN. It enables iterative learning of sentiment dependency information from contextual words to aspect terms. However, directly using syntactic dependency graphs can lead to two shortcomings. First, the long-distance sentiment information in long sentences will be weakened during GCN iterative computations. Second, the syntactic dependency graphs of short sentences lack sentiment strength information. To address these two shortcomings, we propose a Distance-Reconstructed Dependency Enhanced Sentiment analysis approach with Sentiment Strength based on GCN (DRD-SE-GCN) approach, in which the syntactic dependency graph of long sentences is reconstructed based on distance, thus narrowing the gap between sentiment words and target aspect words. It ensures that the sentiment dependency information carried by the long-distance sentiment words will not be weakened or lost during the GCN iteration. Additionally, the weights of the syntactic dependency graph for short sentences are reassigned so as to carry the strength information of sentiment words. Experimental results on four standard datasets show that our proposed model can effectively improve the accuracy of aspect-level sentiment analysis.

Aspect-Level Sentiment Analysis on GoPay App Reviews Using Multilayer Perceptron and Word Embeddings

The increase of smartphone usage in Indonesia has encouraged the development of digital wallet applications, one of which is GoPay. Nowadays, GoPay has gained significant popularity among the public in Indonesia. Therefore, this research conducts aspect-level sentiment analysis to analyze user reviews of the GoPay application in more detail and depth. The sentiment analysis process in this study utilizes the Multilayer Perceptron (MLP) with fastText and word2vec as word embeddings. The dataset used is GoPay application reviews, which consist of 15,000 reviews collected from Google Play Store. The dataset is categorized into three main aspects: Feature and functionality, App Interface, and User Satisfaction. The stages of the research include data preparation, data preprocessing, word embeddings, model training, and model testing and evaluation. This research explores the effect of fastText and word2vec as word embeddings on model performance. Furthermore, this research examines the application of oversampling techniques, such as SMOTE and Random Oversampling. Based on the experiments conducted, utilizing fastText as word embeddings in MLP with a balanced dataset resulted the best model performance, with an F1-Score of 97%, Recall of 96%, and Precision of 95% for aspect category classification. Then, for sentiment classification, using fastText on MLP with a balanced dataset resulted in a value of 98% for each of the F1-score, Recall, and Precision metrics. This research validates that MLP is effective for aspect-level sentiment analysis, delivering strong evaluation results.

DCASAM: advancing aspect-based sentiment analysis through a deep context-aware sentiment analysis model

Aspect-level sentiment analysis plays a pivotal role in fine-grained sentiment categorization, especially given the rapid expansion of online information. Traditional methods often struggle with accurately determining sentiment polarity when faced with implicit or ambiguous data, leading to limited accuracy and context-awareness. To address these challenges, we propose the Deep Context-Aware Sentiment Analysis Model (DCASAM). This model integrates the capabilities of Deep Bidirectional Long Short-Term Memory Network (DBiLSTM) and Densely Connected Graph Convolutional Network (DGCN), enhancing the ability to capture long-distance dependencies and subtle contextual variations.The DBiLSTM component effectively captures sequential dependencies, while the DGCN component leverages densely connected structures to model intricate relationships within the data. This combination allows DCASAM to maintain a high level of contextual understanding and sentiment detection accuracy.Experimental evaluations on well-known public datasets, including Restaurant14, Laptop14, and Twitter, demonstrate the superior performance of DCASAM over existing models. Our model achieves an average improvement in accuracy by 1.07% and F1 score by 1.68%, showcasing its robustness and efficacy in handling complex sentiment analysis tasks.These results highlight the potential of DCASAM for real-world applications, offering a solid foundation for future research in aspect-level sentiment analysis. By providing a more nuanced understanding of sentiment, our model contributes significantly to the advancement of fine-grained sentiment analysis techniques.

An aspect sentiment analysis model with Aspect Gated Convolution and Dual-Feature Filtering layers

Aspect level sentiment analysis is a basic task to determine the sentiment bias based on the contextual information near the aspect words. Some sentences contain many confusing feature words due to incomplete structure or high complexity in sentiment prediction, which can easily lead to the problem that the model pays too much attention to unimportant features. Furthermore, the role of aspect-related sentiment features is not significant in the feature extraction process. The capsule network, due to its complex network structure, leads to capsule detachment when too much information is found in the dataset. Too much computational resources are easily consumed during dynamic routing, which in turn affects the model's judgement of the polarity of aspect-related sentiment. To address these issues, we propose a capsule network (AGCDFF-Caps) with aspect-gated convolution and Dual-Feature Filtering layers for aspect-level sentiment analysis. A pre-trained BERT model is used to generate a serialised representation of the text, and the semantic representation in the contextual text is enhanced by the self-attention mechanism and bi-GRU. An aspect-gating based convolutional neural network is constructed to selectively extract contextual sentiment information about aspects and discard irrelevant information. A capsule network is then used to learn the multispatial semantic features of the aspect and the text. In particular, we incorporate a Dual-Feature Filtering network structure into the capsule network structure to strengthen the interaction between the particular aspect and the context from global and local perspectives, filtering the redundant information in local semantics and global semantics. The opinion feature representations that can more accurately express the emotional tendency of the aspect are obtained. Experimental results on SemEval2014 and Twitter datasets show that the proposed hybrid network structure has superior classification performance compared to 12 advanced baseline methods.

Aspect-based drug review classification through a hybrid model with ant colony optimization using deep learning

The task of aspect-level sentiment analysis is intricately designed to determine the sentiment polarity directed towards a specific target within a sentence. With the increasing availability of online reviews and the growing importance of healthcare decisions, analyzing drug reviews has become a critical task. Traditional sentiment analysis, which categorizes a whole review as positive, negative, or neutral, provides limited insights for consumers and healthcare professionals. Aspect-based sentiment analysis (ABSA) aims to overcome these limitations by identifying and evaluating the sentiment associated with specific aspects or attributes of drugs mentioned in the reviews. Various fields, including business, politics, and medicine, have been explored in the context of sentiment analysis. Automation of online user reviews allows pharmaceutical companies to assess large amounts of user feedback. This helps extract pharmacological efficacy and side effect insights. The data collected could improve pharmacovigilance. Reviewing user comments can provide valuable data that can be used to improve drug safety and efficacy monitoring procedures. This improves pharmacovigilance processes, improving pharmaceutical outcomes understanding and corporate decision-making. Therefore, we propose a pre-trained RoBERTa with a Bi-LSTM model to categorise drug reviews from online sources and pre-process the text data. Ant Colony Optimization can be used in feature selection for ABSA, helping to identify the most relevant aspects and sentiments. Further, RoBERTa is fine-tuned to perform ABSA on the dataset, enabling the system to categorize aspects and determine the associated sentiment. The outcomes reveal that the suggested framework has achieved higher accuracy (96.78%) and F1 score (98.29%) on druglib.com, and 95.02% on the drugs.com dataset, than several prior state-of-the-art methods.

Aspect-level implicit sentiment analysis model based on semantic wave and knowledge enhancement

Aspect-level implicit sentiment analysis model based on semantic wave and knowledge enhancement

A conditioned joint-modality attention fusion approach for multimodal aspect-level sentiment analysis

A conditioned joint-modality attention fusion approach for multimodal aspect-level sentiment analysis

MIECF: Multi-faceted information extraction and cross-mixture fusion for multimodal aspect-based sentiment analysis

Aspect-level sentiment analysis within multimodal contexts, focusing on the precise identification and interpretation of sentiment attitudes linked to the target aspect across diverse data modalities, remains a focal research area that perpetuates the advancement of discourse and innovation in artificial intelligence. However, most existing methods tend to focus on extracting visual features from only one facet, such as face expression, which ignores the value of information from other key facets, such as the textual information presented by the image modality, resulting in information loss. To overcome the aforementioned constraint, we put forth a novel approach designated as Multi-faceted Information Extraction and Cross-mixture Fusion (MIECF) for Multimodal Aspect-based Sentiment Analysis. Our approach captures more comprehensive visual information in the image and integrates these local and global key features from multiple facets. Local features, such as facial expressions and textual features, provide direct and rich emotional cues. By contrast, the global feature often reflects the overall emotional atmosphere and context. To enhance the visual representation, we designed a Cross-mixture Fusion method to integrate this local and global multimodal information. In particular, the method establishes semantic relationships between local and global features to eliminate ambiguity brought by single-facet information and achieve more accurate contextual understanding, providing a richer and more precise manner for sentiment analysis. The experimental findings indicate that our proposed approach achieves a leading level of performance, resulting in an Accuracy of 79.65 % on the Twitter-2015 dataset, and Macro-F1 scores of 75.90 % and 73.11 % for the Twitter-2015 and Twitter-2017 datasets, respectively.

Research on Aspect-Level Sentiment Analysis Based on Adversarial Training and Dependency Parsing

Aspect-level sentiment analysis is used to predict the sentiment polarity of a specific aspect in a sentence. However, most current research cannot fully utilize semantic information, and the models lack robustness. Therefore, this article proposes a model for aspect-level sentiment analysis based on a combination of adversarial training and dependency syntax analysis. First, BERT is used to transform word vectors and construct adjacency matrices with dependency syntactic relationships to better extract semantic dependency relationships and features between sentence components. A multi-head attention mechanism is used to fuse the features of the two parts, simultaneously perform adversarial training on the BERT embedding layer to enhance model robustness, and, finally, to predict emotional polarity. The model was tested on the SemEval 2014 Task 4 dataset. The experimental results showed that, compared with the baseline model, the model achieved significant performance improvement after incorporating adversarial training and dependency syntax relationships.

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.

Manifesto of Deep Learning Architecture for Aspect Level Sentiment Analysis to extract customer criticism

Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.

A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis is a research focal point for natural language comprehension. An attention mechanism is a very important approach for aspect-level sentiment analysis, but it only fuses sentences from a semantic perspective and ignores grammatical information in the sentences. Graph convolutional networks (GCNs) are a better method for processing syntactic information; however, they still face problems in effectively combining semantic and syntactic information. This paper presents a sentiment-supported graph convolutional network (SSGCN). This SSGCN first obtains the semantic information of the text through aspect-aware attention and self-attention; then, a grammar mask matrix and a GCN are applied to preliminarily combine semantic information with grammatical information. Afterward, the processing of these information features is divided into three steps. To begin with, features related to the semantics and grammatical features of aspect words are extracted. The second step obtains the enhanced features of the semantic and grammatical information through sentiment support words. Finally, it concatenates the two features, thus enhancing the effectiveness of the attention mechanism formed from the combination of semantic and grammatical information. The experimental results show that compared with benchmark models, the SSGCN had an improved accuracy of 6.33–0.5%. In macro F1 evaluation, its improvement range was 11.68–0.5%.

Aspect-Level sentiment analysis based on fusion graph double convolutional neural networks

ABSTRACT Aspect-based sentiment analysis (ABSA) aims to analyze the emotional color contained in sentences or documents in more detail by classifying and evaluating different aspects and emotions in the text. However, the current research methods cannot effectively analyze the relationship between aspect words and context and extract grammatical information about sentences. Additionally, the extracted syntactic information is insufficient, and the combination of syntactic and semantic information is inefficient, leaving the model unable to correctly determine aspects' emotional orientations. This paper proposes an aspect-level sentiment analysis based on Fusion Graph Double Convolutional Neural Networks (FGD-GCN) to address these issues. Firstly, FGD-GCN proposes a multi-feature extraction module. Using BERT and bidirectional long-short-term memory models, this module extracts the hidden context between words. In addition, the positional attention module is used to capture important features in sentences, reducing noise and bias. Then, a semantic enhancement module is proposed, which fuses attention-focused information and feature information extracted from graphs to emphasize aspect words and context, and uses CNN model to classify on feature vectors. According to experiment results on three benchmark datasets, the model outperforms previous GCN methods for context-based aspect-level sentiment analysis.

Aspect-Level Sentiment Analysis through Aspect-Oriented Features

Aspect-level sentiment analysis is essential for businesses to comprehend sentiment polarities associated with various aspects within unstructured texts. Although several solutions have been proposed in recent studies in sentiment analysis, a few challenges persist. A significant challenge is the presence of multiple aspects within a single written text, each conveying its own sentiments. Besides this, the exploration of ensemble learning in the existing literature is limited. Therefore, this study proposes a novel aspect-level sentiment analysis solution that utilizes an ensemble of Bidirectional Long Short-Term Memory (BiLSTM) models. This innovative solution extracts aspects and sentiments and incorporates a rule-based algorithm to combine accurate sets of aspect and sentiment features. Experimental analysis demonstrates the effectiveness of the proposed methodology in accurately extracting aspect-level sentiment features from input texts. The proposed solution was able to obtain an F1 score of 92.98% on the SemEval-2014 Restaurant dataset when provided with the correct set of aspect-level sentiment features and an F1 score of 95.54% on the SemEval-2016 Laptop dataset when provided with the aspect-level sentiment features generated by the aspect-sentiment mapper algorithm. Doi: 10.28991/HIJ-2024-05-01-09 Full Text: PDF

Incorporating syntax and semantics with dual graph neural networks for aspect-level sentiment analysis

Aspect-level sentiment analysis is a more fine-grained task that aims to determine the sentiment polarity of specific aspects. Recent studies have employed graph attention networks and graph convolutional networks to model dependency trees, effectively establishing explicit associations between aspects and opinions, yielding promising performance. However, these methods have limitations in capturing complex linguistic features and the intricate dependencies between aspects and their contexts, resulting in suboptimal performance. In this paper, we propose a dual graph neural network that incorporates syntax and semantics, called IDGNN. Specifically, we utilize the relational graph attention network (RGAT) to encode the syntactic dependency tree and obtain syntactic information, while incorporating dependency labels to enhance aspect representation. Additionally, the semantic graph convolutional network (SemGCN) is employed to encode the self-attention matrix and capture semantic information, with the inclusion of orthogonal regularization to enhance semantic association. Furthermore, we introduce two fusion strategies based on gate mechanisms: the syntax fusion module (SYF) and the semantic fusion module (SEF). SYF combines contextual and syntactic representations to obtain global syntactic features, while SEF fuses semantic information with global syntactic features to obtain the final feature representation. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on several benchmark datasets.

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.