Aspect-based Sentiment Analysis Research Articles

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.

Generative Aspect Sentiment Quad Prediction with Self-Inference Template

Aspect Sentiment Quad Prediction is a research topic of paramount significance and complexity within the Aspect-Based Sentiment Analysis task. Leveraging the generative paradigm of the T5 model, we achieve end-to-end extraction of aspect sentiment elements by paraphrasing the original text into sentences predefined by templates. Current research predominantly confines templates to single sentences or directly concatenates sentiment elements using a few symbols, limiting the model’s reasoning opportunities. In this work, we introduce a Self-Inference Template (SIT) to guide the model in thoughtful reasoning, facilitating a step-by-step inference generation process. This approach enables the model to more accurately identify aspect sentiment elements and their interdependencies. Experimental results demonstrate a significant improvement in quadruplet prediction performance under constant time costs, effectively mitigating overfitting issues caused by limited data volume to some extent.

A Knowledge-Driven Approach for Automatic Semantic Aspect Term Extraction Using the Semantic Power of Linked Open Data

Aspect-Based Sentiment Analysis (ABSA) is a crucial process for assessing customer feedback and gauging satisfaction with products or services. It typically consists of three stages: Aspect Term Extraction (ATE), Aspect Categorization Extraction (ACE), and Sentiment Analysis (SA). Various techniques have been proposed for ATE, including unsupervised, supervised, and hybrid methods. However, many studies face challenges in detecting aspect terms due to reliance on training data, which may not cover all multiple aspect terms and relate semantic aspect terms effectively. This study presents a knowledge-driven approach to automatic semantic aspect term extraction from customer feedback using Linked Open Data (LOD) to enrich aspect extraction outcomes in the training dataset. Additionally, it utilizes the N-gram model to capture complex text patterns and relationships, facilitating accurate classification and analysis of multiple-word terms for each aspect. To assess the effectiveness of the proposed model, experiments were conducted on three benchmark datasets: SemEval 2014, 2015, and 2016. Comparative evaluations with contemporary unsupervised, supervised, and hybrid methods on these datasets yielded F-measures of 0.80, 0.76, and 0.77, respectively.

TCKGCN: Graph convolutional network for aspect-based sentiment analysis with three-channel knowledge fusion

At present, most aspect-based sentiment analysis studies addresses the syntactic dependency problem of review statements by building syntactic dependency graphs. However, this approach is insensitive to syntactic structural information and susceptible to noise interference. Existing fusion methods are unable to effectively capture the complementary relationships between information, leading to large and less efficient model structures. To tackle these issues, we introduce a unique and original scheme that fuses semantic information, ConceptNet conceptual knowledge, and SenticNet affective knowledge, intending to improve the capability of the model to capture sentence features. Specifically, the process involves four steps: first, a semantic attention mechanism is constructed as the first channel to parse the semantic relationship within the context; second, the expressive meaning of the aspects is enhanced with concept mapping as the second channel, with concepts injected into aspectual words; third, the representation ability of sentence dependency graphs is strengthened through adding the affective knowledge of SenticNet as a third channel. Subsequently, the sensitivity of the model to affective information is increased, and finally, the optimization of aspect and context coordination is strengthened by integrating an interactive attention mechanism. The overall findings underscore the efficacy of the model, showing accuracy improvements of 1.05 % and 2.80 % on the Twitter and Lap14 datasets, respectively, compared to state-of-the-art models. Moreover the macro-F1 scores showed significant improvements of 1.99 % and 1.61 %, respectively, enabling more effective capturing of aspect-specific sentiment expressions.

Unifying aspect-based sentiment analysis BERT and multi-layered graph convolutional networks for comprehensive sentiment dissection.

Aspect-Based Sentiment Analysis (ABSA) represents a fine-grained approach to sentiment analysis, aiming to pinpoint and evaluate sentiments associated with specific aspects within a text. ABSA encompasses a set of sub-tasks that together facilitate a detailed understanding of the multifaceted sentiment expressions. These tasks include aspect and opinion terms extraction (ATE and OTE), classification of sentiment at the aspect level (ALSC), the coupling of aspect and opinion terms extraction (AOE and AOPE), and the challenging integration of these elements into sentiment triplets (ASTE). Our research introduces a comprehensive framework capable of addressing the entire gamut of ABSA sub-tasks. This framework leverages the contextual strengths of BERT for nuanced language comprehension and employs a biaffine attention mechanism for the precise delineation of word relationships. To address the relational complexity inherent in ABSA, we incorporate a Multi-Layered Enhanced Graph Convolutional Network (MLEGCN) that utilizes advanced linguistic features to refine the model's interpretive capabilities. We also introduce a systematic refinement approach within MLEGCN to enhance word-pair representations, which leverages the implicit outcomes of aspect and opinion extractions to ascertain the compatibility of word pairs. We conduct extensive experiments on benchmark datasets, where our model significantly outperforms existing approaches. Our contributions establish a new paradigm for sentiment analysis, offering a robust tool for the nuanced extraction of sentiment information across diverse text corpora. This work is anticipated to have significant implications for the advancement of sentiment analysis technology, providing deeper insights into consumer preferences and opinions for a wide range of applications.

Aspect-specific Parsimonious Segmentation via Attention-based Graph Convolutional Network for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) involves determining sentiment polarity in sentences concerning specific aspects. Recent approaches in ABSA have incorporated dependency parsing to gain a deeper understanding of word associations within aspect-related terms. Nevertheless, these models typically concentrate solely on dependency connections while neglecting their specific characteristics or types. This can lead to sentiment inconsistencies, particularly when analyzing sentences with multiple aspects and diverse polarities. To address this challenge, we propose an innovative solution known as Aspect-specific Parsimonious Segmentation via Attention-based Graph Convolutional Network (APSA-GCN) for ABSA. Our model consists of two key modules that harness a variety of features, including syntax, semantics, and lexicon-based representations. The aspect-specific parsimonious segmentation framework segments the data and directs it to parsimonious dual filters, retaining only aspect-specific sentences called explicit data for lexicon-based representation. Subsequently, the Attention-based Graph Convolutional Network (GCN) is employed to extract syntactic and semantic features more explicitly, thereby enriching the representation of aspect-related features. Extensive experiments were conducted on various benchmark datasets, and the results confirm the model’s efficacy as our method achieves state-of-the-art performance. Moreover, our introduced APS framework can be effortlessly incorporated into other benchmark models to enhance the outcomes of ABSA.

The Impact of Oversampling and Undersampling on Aspect-Based Sentiment Analysis of Indramayu Tourism Using Logistic Regression

The Impact of Oversampling and Undersampling on Aspect-Based Sentiment Analysis of Indramayu Tourism Using Logistic Regression

Evaluating Tourist Dissatisfaction with Aspect-Based Sentiment Analysis Using Social Media Data

Tourism satisfaction is essential for encouraging tourists to stay longer, spend more and return. However, visitor dissatisfaction can also prove useful for understanding any shortcomings of a tourist destination, and Twitter, Instagram and TripAdvisor reviews might be able to provide an insight into tourist perceptions and experiences. This study examines the major causes of tourist dissatisfaction with a tourism destination using an aspect-based sentiment analysis approach to understand the key points of negative tweets, posts or reviews. We examined 19,340 tweets, 7,712 Instagram posts and 25,483 reviews about Granada in Spain in order to evaluate the negative user's perceptions, discover management-related problems and provide feedback to destination management organizations to enable them to improve their services and operations. Our work contributes to computational methods to address tourism (dis)satisfaction with a process to identify the most important entities (places), an algorithm to identify aspects and opinions, and the use of word-trees to show the most important aspect-opinion tuples. In practical terms, we provide to tourism industry professionals and managers, as well as travelers, with methods to identify the reasons for tourist dissatisfaction from available social media data, in such a way that managerial strategies or travel plans can be improved.

Evaluating Tourist Dissatisfaction with Aspect-Based Sentiment Analysis Using Social Media Data

Tourism satisfaction is essential for encouraging tourists to stay longer, spend more and return. However, visitor dissatisfaction can also prove useful for understanding any shortcomings of a tourist destination, and Twitter, Instagram and TripAdvisor reviews might be able to provide an insight into tourist perceptions and experiences. This study examines the major causes of tourist dissatisfaction with a tourism destination using an aspect-based sentiment analysis approach to understand the key points of negative tweets, posts or reviews. We examined 19,340 tweets, 7,712 Instagram posts and 25,483 reviews about Granada in Spain in order to evaluate the negative user's perceptions, discover management-related problems and provide feedback to destination management organizations to enable them to improve their services and operations. Our work contributes to computational methods to address tourism (dis)satisfaction with a process to identify the most important entities (places), an algorithm to identify aspects and opinions, and the use of word-trees to show the most important aspect-opinion tuples. In practical terms, we provide to tourism industry professionals and managers, as well as travelers, with methods to identify the reasons for tourist dissatisfaction from available social media data, in such a way that managerial strategies or travel plans can be improved.

Aspect based sentiment analysis using a novel ensemble deep network

Aspect-based sentiment analysis (ABSA) is a fine-grained task in natural language processing, which aims to predict the sentiment polarity of several parts of a sentence or document. The essential aspect of sentiment polarity and global context have deep relationships that have not received enough attention. This research work design and develops a novel ensemble deep network (EDN) which comprises the various network and integrated to enhance the model performance. In the proposed work the words of the input sentence are converted into word vectors using the optimised bidirectional encoder representations from transformers (BERT) model and an optimised BERT-graph neural networks (GNN) model with convolutions is built that analyses the ABSA of the input sentence. The optimised GNN model with convolutions for context-based word representations is developed for the word-vector embedding. We propose a novel EDN for an ABSA model for optimised BERT over GNN with convolutions. The proposed ensemble deep network proposed system (EDN-PS) is evaluated with various existing techniques and results are plotted in terms of metrics for accuracy and F1- score, concluding that the proposed EDN-PS ensures better performance in comparison with the existing model.

Aspect based sentiment analysis using fine-tuned BERT model with deep context features

<p>Sentiment analysis is the task of analysing, processing, inferencing and concluding the subjective texts along with sentiment. Considering the application of sentiment analysis, it is categorized into document-level, sentence-level and aspect level. In past, several researches have achieved solutions through the bidirectional encoder representations from transformers (BERT) model, however, the existing model does not understand the context of the aspect in deep, which leads to low metrics. This research work leads to the study of the aspect-based sentiment analysis presented by deep context bidirectional encoder representations from transformers (DC-BERT), main aim of the DC-BERT model is to improvise the context understating for aspects to enhance the metrics. DC-BERT model comprises fine-tuned BERT model along with a deep context features layer, which enables the model to understand the context of targeted aspects deeply. A customized feature layer is introduced to extract two distinctive features, later both features are integrated through the interaction layer. DC-BERT mode is evaluated considering the review dataset of laptops and restaurants from SemEval 2014 task 4, evaluation is carried out considering the different metrics. In comparison with the other model, DC-BERT achieves an accuracy of 84.48% and 92.86% for laptop and restaurant datasets respectively.</p>

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

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

Determining community happiness index with transformers and attention-based deep learning

In the current digital era, evaluating the quality of people's lives and their happiness index is closely related to their expressions and opinions on Twitter social media. Measuring population welfare goes beyond monetary aspects, focusing more on subjective well-being, and sentiment analysis helps evaluate people's perceptions of happiness aspects. Aspect-based sentiment analysis (ABSA) effectively identifies sentiments on predetermined aspects. The previous study has used Word-to-Vector (Word2Vec) and long short-term memory (LSTM) methods with or without attention mechanism (AM) to solve ABSA cases. However, the problem with the previous study is that Word2Vec has the disadvantage of being unable to handle the context of words in a sentence. Therefore, this study will address the problem with bidirectional encoder representations from transformers (BERT), which has the advantage of performing bidirectional training. Bayesian optimization as a hyperparameter tuning technique is used to find the best combination of parameters during the training process. Here we show that BERT-LSTM-AM outperforms the Word2Vec-LSTM-AM model in predicting aspect and sentiment. Furthermore, we found that BERT is the best state-of-the-art embedding technique for representing words in a sentence. Our results demonstrate how BERT as an embedding technique can significantly improve the model performance over Word2Vec.

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.

Progressive Multigranularity Information Propagation for Coupled Aspect-Opinion Extraction.

Coupled aspect-opinion extraction aims to identify aspect-opinion pairs in the form of (aspect term, opinion term) or triplets in the form of (aspect term, opinion term, sentiment polarity) from user-generated texts. Compared to the traditional aspect-based sentiment prediction or extraction tasks, coupled aspect-opinion extraction needs to associate aspects with their corresponding opinions and organize opinion-related information into structured outputs. The existing works either divide this task into subproblems (i.e., term extraction and relation prediction) or utilize a unified tagging scheme. However, these methods only focus on atomic word-level interactions and ignore the intensive information propagation among different granularities (e.g., words and word pairs). To address this limitation, we propose a progressive multigranularity information propagation network that progressively explores three types of correlations with different granularities. Specifically, our model starts with the most basic word-level correlations by composing all possible word pairs. In the second stage, the pairwise relation information is used to update the word features. The last stage propagates information among word pairs to produce the relation scores. We treat the task as a unified relation prediction problem and construct an end-to-end framework that iteratively conducts the three-stage information propagation to refine the textual representations. Comprehensive experiments on different aspect-based sentiment analysis benchmarks clearly demonstrate the effectiveness of the proposed approach.

DualGCN: Exploring Syntactic and Semantic Information for Aspect-Based Sentiment Analysis.

The task of aspect-based sentiment analysis aims to identify sentiment polarities of given aspects in a sentence. Recent advances have demonstrated the advantage of incorporating the syntactic dependency structure with graph convolutional networks (GCNs). However, their performance of these GCN-based methods largely depends on the dependency parsers, which would produce diverse parsing results for a sentence. In this article, we propose a dual GCN (DualGCN) that jointly considers the syntax structures and semantic correlations. Our DualGCN model mainly comprises four modules: 1) SynGCN: instead of explicitly encoding syntactic structure, the SynGCN module uses the dependency probability matrix as a graph structure to implicitly integrate the syntactic information; 2) SemGCN: we design the SemGCN module with multihead attention to enhance the performance of the syntactic structure with the semantic information; 3) Regularizers: we propose orthogonal and differential regularizers to precisely capture semantic correlations between words by constraining attention scores in the SemGCN module; and 4) Mutual BiAffine: we use the BiAffine module to bridge relevant information between the SynGCN and SemGCN modules. Extensive experiments are conducted compared with up-to-date pretrained language encoders on two groups of datasets, one including Restaurant14, Laptop14, and Twitter and the other including Restaurant15 and Restaurant16. The experimental results demonstrate that the parsing results of various dependency parsers affect their performance of the GCN-based models. Our DualGCN model achieves superior performance compared with the state-of-the-art approaches. The source code and preprocessed datasets are provided and publicly available on GitHub (see https://github.com/CCChenhao997/DualGCN-ABSA).

Prompt-based data labeling method for aspect based sentiment analysis

Prompt-based data labeling method for aspect based sentiment analysis

Comprehensive Exploration of Machine and Deep Learning Classification Methods for Aspect-Based Sentiment Analysis with Latent Dirichlet Allocation Topic Modeling

This research explores the effectiveness of machine learning (ML) and deep learning (DL) classification methods in Aspect-Based Sentiment Analysis (ABSA) on product reviews, incorporating Latent Dirichlet Allocation (LDA) for topic modeling. Using the Amazon reviews dataset, this research tests models such as Naive Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Units(GRU). Important aspects such as the product's quality, practicality, and reliability are discussed. The results show that the RF and DL models provide competitive performance, with the RF achieving an accuracy of up to 94.50% and an F1 score of 95.45% for the reliability aspect. The study's conclusions emphasize the importance of selecting an appropriate model based on specifications and data requirements for ABSA, as well as recognizing the need to strike a balance between accuracy and computational efficiency.

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics.

In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensemble (MLEn), tackles the intricate task of extracting multi-labeled data, employing advanced techniques for accurate preprocessing of textual data through the NLTK toolkit. This approach is carefully tailored to the prevailing language used in logistics communication. MLEn utilizes innovative methods, including sentiment intensity analysis, Word2Vec, and Doc2Vec, ensuring comprehensive feature extraction. This proves particularly suitable for logistics in e-commerce, capturing nuanced communication essential for efficient operations. Ethical considerations are a cornerstone in logistics communication, and MLEn plays a pivotal role in detecting and categorizing inappropriate language, aligning inherently with ethical norms. Leveraging Tf-IDF and Vader for feature enhancement, MLEn adeptly discerns and labels ethically sensitive content in logistics communication. Across diverse datasets, including Emotions, MLEn consistently achieves impressive accuracy levels ranging from 92% to 97%, establishing its superiority in the logistics context. Particularly, our proposed method, DenseNet-EHO, outperforms BERT by 8% and surpasses other techniques by a 15-25% efficiency. A comprehensive analysis, considering metrics such as precision, recall, F1-score, Ranking Loss, Jaccard Similarity, AUC-ROC, sensitivity, and time complexity, underscores DenseNet-EHO's efficiency, aligning with the practical demands within the logistics track. Our research significantly contributes to enhancing precision, diversity, and computational efficiency in aspect-based sentiment analysis within logistics. By integrating cutting-edge preprocessing, sentiment intensity analysis, and vectorization, MLEn emerges as a robust framework for multi-label datasets, consistently outperforming conventional approaches and giving outstanding precision, accuracy, and efficiency in the logistics field.

Bi-syntax guided transformer network for aspect sentiment triplet extraction

Aspect Sentiment Triplet Extraction is an emerging and challenging task that attempts to present a complete picture of aspect-based sentiment analysis. Prior research efforts mostly leverage various tagging schemes to extract the three elements in a triplet. However, these methods fail to explicitly model the complicated relations between aspects and opinions and the boundaries of multi-word aspects and opinions. In this paper, we propose a bi-syntax guided transformer network in an end-to-end manner to address these challenges. Firstly, we devise three types of representations, including sequence distance representation, constituency distance representation, and dependency distance representation, to learn the comprehensive language representation. Specifically, sequence distance representation utilizes sequence distance between words to enhance the contextual representation. Constituency distance representation adopts constituency distance between words in a constituency tree to capture the intra-span relation between words. Dependency distance representation employs dependency distance between words in a dependency tree to capture the long-distance relation between aspects and opinions. Extensive experiments are conducted on four benchmark datasets to validate the effectiveness of our method. The results demonstrate that the proposed approach achieves better performance than baseline methods. We conduct further detailed analysis to demonstrate that our method effectively handles multi-word terms and overlapping triplets.