Aspect-level Sentiment Analysis Research Articles

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

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

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

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.

Extracting Explicit and Implicit Aspects Using Deep Learning

The proliferation of user-generated content on social networks and websites has heightened the significance of sentiment analysis, also known as opinion mining, as a critical tool for comprehending people’s attitudes toward various topics. Aspect-level sentiment analysis, which considers specific aspects or features of texts, provides a more comprehensive view of sentiment analysis. The aspect-level approach encompasses both explicit and implicit aspects, where explicit aspects are readily mentioned in texts while implicit aspects are implied or inferred from contextual clues. Despite the significance of implicit aspects in the overall review, previous research has predominantly focused on explicit aspect extraction. Limited attention has been given to the extraction of implicit aspects, despite their potential impact on capturing the complete sentiment picture of texts. Therefore, this study aims to find an aspect extraction solution capable of identifying and extracting both explicit and implicit aspects from texts. This study compares various machine and deep learning models on the SemEval-2014 and SemEval-2016 restaurant datasets. The experimental analysis demonstrates that the proposed Aspect-BiLSTM model emerged as the best-performing model, achieving high accuracy in classifying both explicit and implicit aspects, with 92.9% accuracy for the 2014 and 90.7% accuracy for the 2016 datasets. Notably, the proposed solution was able to capture multiple aspects of texts, making it more robust and versatile. This study highlights the efficacy of the Aspect-BiLSTM model for aspect extraction, which will give valuable insights into the advancement of aspect-level sentiment analysis. Doi: 10.28991/ESJ-2024-08-01-05 Full Text: PDF

Aspect-level multimodal sentiment analysis based on co-attention fusion

AbstractAspect-level multimodal sentiment analysis is the fine-grained sentiment analysis task of predicting the sentiment polarity of given aspects in multimodal data. Most existing multimodal sentiment analysis approaches focus on mining and fusing multimodal global features, overlooking the correlation of more fine-grained multimodal local features, which considerably limits the semantic relevance between different modalities. Therefore, a novel aspect-level multimodal sentiment analysis method based on global–local features fusion with co-attention (GLFFCA) is proposed to comprehensively explore multimodal associations from both global and local perspectives. Specially, an aspect-guided global co-attention module is designed to capture aspect-guided intra-modality global correlations. Meanwhile, a gated local co-attention module is introduced to capture the adaptive association alignment of multimodal local features. Following that, a global–local multimodal feature fusion module is constructed to integrate global–local multimodal features in a hierarchical manner. Extensive experiments on the Twitter-2015 dataset and Twitter-2017 dataset validate the effectiveness of the proposed method, which can achieve better aspect-level multimodal sentiment analysis performance compared with other related methods.

Aspect-Level Sentiment Analysis Based on Syntax-Aware and Graph Convolutional Networks

Aspect-level sentiment analysis is a task of identifying and understanding the sentiment polarity of specific aspects of a sentence. In recent years, significant progress has been made in aspect-level sentiment analysis models based on graph convolutional neural networks. However, existing models still have some shortcomings, such as aspect-level sentiment analysis models based on graph convolutional networks not making full use of the information of specific aspects in a sentence and ignoring the enhancement of the model by external general knowledge of sentiment. In order to solve these problems, this paper proposes a sentiment analysis model based on the Syntax-Aware and Graph Convolutional Network (SAGCN). The model first integrates aspect-specific features into contextual information, and second incorporates external sentiment knowledge to enhance the model’s ability to perceive sentiment information. Finally, a multi-head self-attention mechanism and Point-wise Convolutional Transformer (PCT) are applied to capture the semantic information of the sentence. The semantic and syntactic information of the sentences are considered together. Experimental results on three benchmark datasets show that the SAGCN model is able to achieve superior performance compared to the benchmark methods.

Word distance assisted dual graph convolutional networks for accurate and fast aspect-level sentiment analysis.

Aspect-level sentiment analysis can provide a fine-grain sentiment classification for inferring the sentiment polarity of specific aspects. Graph convolutional network (GCN) becomes increasingly popular because its graph structure can characterize the words' correlation for extracting more sentiment information. However, the word distance is often ignored and cause the cross-misclassification of different aspects. To address the problem, we propose a novel dual GCN structure to take advantage of word distance, syntactic information, and sentiment knowledge in a joint way. The word distance is not only used to enhance the syntactic dependency tree, but also to construct a new graph with semantic knowledge. Then, the two kinds of word distance assisted graphs are fed into two GCNs for further classification. The comprehensive results on two self-collected Chinese datasets (MOOC comments and Douban book reviews) as well as five open-source English datasets, demonstrate that our proposed approach achieves higher classification accuracy than the state-of-the-art methods with up to 1.81x training acceleration.

Deep Learning Model for Interpretability and Explainability of Aspect-Level Sentiment Analysis Based on Social Media

Deep Learning Model for Interpretability and Explainability of Aspect-Level Sentiment Analysis Based on Social Media

Enhanced local knowledge with proximity values and syntax-clusters for aspect-level sentiment analysis

Aspect-level sentiment analysis (ALSA) aims to extract the polarity of different aspect terms in a sentence. Previous works leveraging traditional dependency syntax parsing trees (DSPT) to encode contextual syntactic information had obtained state-of-the-art results. However, these works may not be able to learn fine-grained syntactic knowledge efficiently, which makes them difficult to take advantage of local context. Furthermore, these works failed to exploit the dependency relation from DSPT sufficiently. To solve these problems, we propose a novel method to enhance local knowledge by using extensions of Local Context Network based on Proximity Values (LCPV) and Syntax-clusters Attention (SCA), named LCSA. LCPV first gets the induced trees from pre-trained models and generates the syntactic proximity values between context word and aspect to adaptively determine the extent of local context. Our improved SCA further extracts fine-grained knowledge, which not only focuses on the essential clusters for the target aspect term but also guides the model to learn essential words inside each cluster in DSPT. Extensive experimental results on multiple benchmark datasets demonstrate that LCSA is highly robust and achieves state-of-the-art performance for ALSA.

Aspect-level sentiment analysis based on aspect-sentence graph convolution network

Aspect-level sentiment analysis aims to identify the sentiment polarity of aspect words in sentences. The existing research methods only focus on the grammatical dependencies between words, ignoring the semantic connections between aspect words and the dependency types between words, which limits the performance of the aspect-level sentiment analysis model. Therefore, this paper proposes an aspect-sentence Graph Convolutional Networks model (ASGCN) to perceive more comprehensive semantic information. Specifically, the model consists of sentence-focused GCN (SentenceGCN) and aspects-focused GCN (AspectsGCN) sub models. In the SentenceGCN model, this paper proposes a method to calculate the adjacency matrix (As) of syntactic dependency graph, which uses the position encoding mechanism and pays attention to the influence of different dependency types on semantics, so that SentenceGCN can capture the semantic information of the whole sentence more comprehensively. In the AspectsGCN model, this paper also proposes a method to calculate the adjacency matrix (Aa) of aspect words, which models the relational graph as fully connected and gives weight to the edges between aspect words according to the position, so that the AspectsGCN can pay attention to the semantic relation between different aspect words in the sentence. The proposed model outperforms all baseline models with 86.34 % accuracy and 79.96 F1 score, which indicates that there are more advantages in perceiving semantic information in ASGCN.

Aspect Level Sentiment Analysis Based on Deep Learning and Ontologies

Aspect Level Sentiment Analysis Based on Deep Learning and Ontologies

A knowledge-augmented heterogeneous graph convolutional network for aspect-level multimodal sentiment analysis

Aspect-level multimodal sentiment analysis has also become a new challenge in the field of sentiment analysis. Although there has been significant progress in the task based on image–text data, existing works do not fully deal with the implicit sentiment expression in data. In addition, they do not fully exploit the important information from external knowledge and image tags. To address these problems, we propose a knowledge-augmented heterogeneous graph convolutional network (KAHGCN). First, we propose a dynamic knowledge selection algorithm to select the most relevant external knowledge, thereby enhancing KAHGCN’s ability of understanding the implicit sentiment expression in review texts. Second, we propose a graph construction strategy to construct a heterogeneous graph that aggregates review text, image tags and external knowledge. Third, we propose a multilayer heterogeneous graph convolutional network to strengthen the interaction between information from external knowledge, review texts and image tags. Experimental results on two datasets demonstrate the effectiveness of the KAHGCN.

Base on contextual phrases with cross-correlation attention for aspect-level sentiment analysis

In recent years, sentiment analysis has emerged as a prominent area of research within the field of natural language processing. Particularly, aspect-level sentiment classification has gained significant attention for its focus on discerning and analyzing sentiment expressed towards specific aspects within sentences. Existing methods primarily rely on extracting keywords from sentence contexts to determine sentiment polarity, yielding satisfactory results. However, a notable limitation of these approaches is their inability to consider the crucial information contained within key phrases in sentences, which plays a vital role in sentiment analysis. To address this limitation, we propose a novel deformable convolutional network model designed to leverage the power of phrases for aspect-level sentiment analysis. By utilizing deformable convolutions with adaptive receptive fields, our model effectively extracts phrase representations at various contextual distances. Furthermore, a cross-correlation attention mechanism is incorporated to capture interdependencies between phrases and words in the context. To evaluate the effectiveness of our approach, we conduct comprehensive evaluations across widely used datasets, demonstrating the promising performance of our model in enhancing sentiment classification tasks. Our model outperforms the model based on CNN, which also leverages phrase extraction, by improving accuracy by 1.71%, 2.5%, and 1.89%, respectively, on the Laptop, Restaurant, and Twitter datasets. Additionally, it surpasses the performance of the latest models.

Triple-channel graph attention network for improving aspect-level sentiment analysis

Triple-channel graph attention network for improving aspect-level sentiment analysis

A user review data-driven supplier ranking model using aspect-based sentiment analysis and fuzzy theory

Background:The supplier selection problem is a sophisticated decision-making process that involves evaluating multiple factors. While previous research has primarily focused on objective attributes, such as supplier qualifications, product quality, and price, the subjective opinions of users have often been overlooked. However, with the growing importance of user reviews and sentiment analysis in e-commerce, incorporating users’ opinions on supplier products can provide valuable insights. Purpose:This study aims to address the limitations of existing supplier selection approaches by proposing a comprehensive framework that integrates aspect-level sentiment analysis and a fuzzy multi-attribute decision model. The goal is to enhance the decision-making process by considering both objective attributes and subjective opinions. Methods:To achieve this, we develop a novel convolutional neural network (CNN) model with a gating mechanism to perform aspect-level sentiment analysis. Furthermore, we propose a fuzzy multi-attribute decision model that combines the predefined sentiment aspects with traditional evaluation criteria. The model is applied to a dataset specifically designed for automotive component supplier selection. Results:Experimental results demonstrate the superior performance of our approach compared to existing methods and datasets. A case study demonstrates the combination of aspect-level sentiment analysis and the fuzzy decision model allows for a more comprehensive evaluation of suppliers. Conclusion:By integrating aspect-level sentiment analysis and the fuzzy multi-attribute decision model, our proposed framework offers a novel perspective on supplier selection problems. The results highlight the feasibility and superiority of our approach, providing valuable insights for management in making informed decisions. This research contributes to the fields of supplier selection, sentiment analysis, and decision-making, with potential applications in various industries beyond the automotive sector.

An aspect-level sentiment analysis dataset for therapies on Twitter.

The dataset described is an aspect-level sentiment analysis dataset for therapies, including medication, behavioral and other therapies, created by leveraging user-generated text from Twitter. The dataset was constructed by collecting Twitter posts using keywords associated with the therapies (often referred to as treatments). Subsequently, subsets of the collected posts were manually reviewed, and annotation guidelines were developed to categorize the posts as positive, negative, or neutral. The dataset contains a total of 5364 posts mentioning 32 therapies. These posts are further categorized manually into 998 (18.6%) positive, 619 (11.5%) negatives, and 3747 (69.9%) neutral sentiments. The inter-annotation agreement for the dataset was evaluated using Cohen's Kappa score, achieving an 0.82 score. The potential use of this dataset lies in the development of automatic systems that can detect users' sentiments toward therapies based on their posts. While there are other sentiment analysis datasets available, this is the first that encodes sentiments associated with specific therapies. Researchers and developers can utilize this dataset to train sentiment analysis models, natural language processing algorithms, or machine learning systems to accurately identify and analyze the sentiments expressed by consumers on social media platforms like Twitter.