Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.展开更多
Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some sentiment.Therefore,sentiment analysis is currently a hot topic of research.In particula...Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some sentiment.Therefore,sentiment analysis is currently a hot topic of research.In particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of polarity.It is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or neutral.Currently,the available lexicon-based method accuracy is affected by limited coverage.Several of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different topics.This paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify ABSA-PER.Experimental outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.展开更多
In the era of digital communication,the exponential growth of user-generated content across social media and online platforms has intensified the demand for effective emotion analysis tools.Traditional text-based sent...In the era of digital communication,the exponential growth of user-generated content across social media and online platforms has intensified the demand for effective emotion analysis tools.Traditional text-based sentiment analysis methods,however,often fall short in accurately capturing the nuances of human emotions due to their reliance on a single modality.Motivated by the need for more comprehensive and context-aware emotion recognition,this study systematically reviews the literature on both unimodal and multimodal aspect-level sentiment analysis.By comparing different approaches within the multimodal domain,we identify existing challenges and emerging trends in this research area.Our findings highlight the potential of integrating multiple modalities—such as text,images,and audio—to enhance the precision of sentiment detection and suggest future directions for advancing multimodal sentiment analysis.展开更多
1 Introduction This paper focuses on sentiment quadruple extraction in Chinese aspect-based sentiment analysis(ABSA),which involves identifying aspect terms,categories,opinion terms,and sentiment polarities.While exis...1 Introduction This paper focuses on sentiment quadruple extraction in Chinese aspect-based sentiment analysis(ABSA),which involves identifying aspect terms,categories,opinion terms,and sentiment polarities.While existing studies focus solely on text-based analysis,we propose multimodal Chinese ABSA,integrating text and acoustic content to leverage their complementary roles.Motivated by the unique linguistic and emotional cues in speech,we construct the first large-scale Chinese ABSA dataset(12,918 sentences,157 hours of speech)with full annotations for all four sentiment elements.In Chinese,prosodic pauses and pitch variations inherently encode linguistic boundaries,enabling accurate word segmentation.Additionally,acoustic signals resolve ambiguities in multisyllabic homographs(e.g.,distinguishing meanings via pronunciation)and disambiguate textual uncertainties through phonetic nuances.展开更多
With the growing demand formore comprehensive and nuanced sentiment understanding,Multimodal Sentiment Analysis(MSA)has gained significant traction in recent years and continues to attract widespread attention in the ...With the growing demand formore comprehensive and nuanced sentiment understanding,Multimodal Sentiment Analysis(MSA)has gained significant traction in recent years and continues to attract widespread attention in the academic community.Despite notable advances,existing approaches still face critical challenges in both information modeling and modality fusion.On one hand,many current methods rely heavily on encoders to extract global features from each modality,which limits their ability to capture latent fine-grained emotional cues within modalities.On the other hand,prevailing fusion strategies often lack mechanisms to model semantic discrepancies across modalities and to adaptively regulate modality interactions.To address these limitations,we propose a novel framework for MSA,termed Multi-Granularity Guided Fusion(MGGF).The proposed framework consists of three core components:(i)Multi-Granularity Feature Extraction Module,which simultaneously captures both global and local emotional features within each modality,and integrates them to construct richer intra-modal representations;(ii)Cross-ModalGuidance Learning Module(CMGL),which introduces a cross-modal scoring mechanism to quantify the divergence and complementarity betweenmodalities.These scores are then used as guiding signals to enable the fusion strategy to adaptively respond to scenarios of modality agreement or conflict;(iii)Cross-Modal Fusion Module(CMF),which learns the semantic dependencies among modalities and facilitates deep-level emotional feature interaction,thereby enhancing sentiment prediction with complementary information.We evaluate MGGF on two benchmark datasets:MVSA-Single and MVSA-Multiple.Experimental results demonstrate that MGGF outperforms the current state-of-the-art model CLMLF on MVSA-Single by achieving a 2.32% improvement in F1 score.On MVSA-Multiple,it surpasses MGNNS with a 0.26% increase in accuracy.These results substantiate the effectiveness ofMGGFin addressing two major limitations of existing methods—insufficient intra-modal fine-grained sentiment modeling and inadequate cross-modal semantic fusion.展开更多
The aspect-based sentiment analysis(ABSA)consists of two subtasksaspect term extraction and aspect sentiment prediction.Most methods conduct the ABSA task by handling the subtasks in a pipeline manner,whereby problems...The aspect-based sentiment analysis(ABSA)consists of two subtasksaspect term extraction and aspect sentiment prediction.Most methods conduct the ABSA task by handling the subtasks in a pipeline manner,whereby problems in performance and real application emerge.In this study,we propose an end-to-end ABSA model,namely,SSi-LSi,which fuses the syntactic structure information and the lexical semantic information,to address the limitation that existing end-to-end methods do not fully exploit the text information.Through two network branches,the model extracts syntactic structure information and lexical semantic information,which integrates the part of speech,sememes,and context,respectively.Then,on the basis of an attention mechanism,the model further realizes the fusion of the syntactic structure information and the lexical semantic information to obtain higher quality ABSA results,in which way the text information is fully used.Subsequent experiments demonstrate that the SSi-LSi model has certain advantages in using different text information.展开更多
The aspect-based sentiment analysis(ABSA) consists of two subtasks—aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies...The aspect-based sentiment analysis(ABSA) consists of two subtasks—aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network(MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. Meanwhile, the representation obtained from the branch network of the main task is regarded as the global view, whereas the representations of the two subtasks are considered two local views with different emphases. Through multitask learning, the main task can be facilitated by additional accurate aspect boundary information and sentiment polarity information. By enhancing the correlations between the views under the idea of multiview learning, the representation of the global view can be optimized to improve the overall performance of the model. The experimental results on three benchmark datasets show that the proposed method exceeds the existing pipeline methods and end-to-end methods, proving the superiority of our MTMVN architecture.展开更多
The Aspect-Based Sentiment Analysis(ABSA)task is designed to judge the sentiment polarity of a particular aspect in a review.Recent studies have proved that GCN can capture syntactic and semantic features from depende...The Aspect-Based Sentiment Analysis(ABSA)task is designed to judge the sentiment polarity of a particular aspect in a review.Recent studies have proved that GCN can capture syntactic and semantic features from dependency graphs generated by dependency trees and semantic graphs generated by Multi-headed self-attention(MHSA).However,these approaches do not highlight the sentiment information associated with aspect in the syntactic and semantic graphs.We propose the Aspect-Guided Multi-Graph Convolutional Networks(AGGCN)for Aspect-Based Sentiment Classification.Specifically,we reconstruct two kinds of graphs,changing the weight of the dependency graph by distance from aspect and improving the semantic graph by Aspect-guided MHSA.For interactive learning of syntax and semantics,we dynamically fuse syntactic and semantic diagrams to generate syntactic-semantic graphs to learn emotional features jointly.In addition,Multi-dropout is added to solve the overftting of AGGCN in training.The experimental results on extensive datasets show that our model AGGCN achieves particularly advanced results and validates the effectiveness of the model.展开更多
Peer reviews of academic articles contain reviewers’ overall impressions and specific comments on the contributed articles,which have a lot of sentimental information.By exploring the fine-grained sentiments in peer ...Peer reviews of academic articles contain reviewers’ overall impressions and specific comments on the contributed articles,which have a lot of sentimental information.By exploring the fine-grained sentiments in peer reviews,we can discover critical aspects of interest to the reviewers.The results can also assist editors and chairmen in making final decisions.However,current research on the aspects of peer reviews is coarse-grained,and mostly focuses on the overall evaluation of the review objects.Therefore,this paper constructs a multi-level fine-grained aspect set of peer reviews for further study.First,this paper uses the multi-level aspect extraction method to extract the aspects from peer reviews of ICLR conference papers.Comparative experiments confirm the validity of the method.Secondly,various Deep Learning models are used to classify aspects’ sentiments automatically,with LCFS-BERT performing best.By calculating the correlation between sentimental scores of the review aspects and the acceptance result of papers,we can find the important aspects affecting acceptance.Finally,this paper predicts acceptance results of papers(accepted/rejected) according to the peer reviews.The optimal acceptance prediction model is XGboost,achieving a Macro_F1 score of 87.43%.展开更多
Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing...Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing the way devices interact and communicate in dynamic and uncertain situations.Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system.Therefore,it leads to the design of effective and trusted routing to support sustainable smart cities.This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model,which combines big data analytics and analysis rules to evaluate user feedback.The sentiment analysis is utilized to assess the perception of network performance,allowing the classification of device behavior as positive,neutral,or negative.By integrating sentiment-driven insights,the IoT network adjusts the system configurations to enhance the performance using network behaviour in terms of latency,reliability,fault tolerance,and sentiment score.Accordingly to the analysis,the proposed model categorizes the behavior of devices as positive,neutral,or negative,facilitating real-time monitoring for crucial applications.Experimental results revealed a significant improvement in the proposed model for threat prevention and network efficiency,demonstrating its resilience for real-time IoT applications.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of...The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements.Traditional sentiment measurement methods suffer from limitations such as lag,high misjudgment rates,and the inability to distinguish confounding factors.To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations,this paper proposes a sentiment analysis framework based on large language models(LLMs).By constructing continuous sentiment scoring factors and integrating them with a long short-term memory(LSTM)deep learning model,we analyze the correlation between investor sentiment and stock price fluctuations.Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3%in the CSI 500 index domain.The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error(MAPE)as low as 2.72%,significantly outperforming traditional models.Through this analysis,we aim to provide quantitative references for optimizing investment decisions and preventing market risks.展开更多
Theburgeoning e-commerce industry hasmade online customer reviews a crucial source of feedback for businesses.Sentiment analysis,a technique used to extract subjective information from text,has become essential for un...Theburgeoning e-commerce industry hasmade online customer reviews a crucial source of feedback for businesses.Sentiment analysis,a technique used to extract subjective information from text,has become essential for understanding consumer sentiment and preferences.However,traditional sentiment analysis methods often struggle with the nuances and context of natural language.To address these issues,this study proposes a comparison of deep learningmodels that figure out the optimalmethod to accurately analyze consumer reviews onwomen’s clothing.CNNs excel at capturing local features and semantic information,while LSTMs are adept at handling long-range dependencies and contextual understanding.By integrating these two deep learning techniques,our model aims to achieve better performance in sentiment classification.The models were trained and evaluated on a dataset of women’s clothing reviews sourced from Kaggle.The dataset was pre-processed to clean and tokenize the text data,and word embeddings were used to represent words as numerical vectors.The CNN component of the model extracts local features from the text,while the LSTM component captures long-range dependencies and contextual information.The outputs of the CNN and LSTM layers are then concatenated and fed into a fully connected layer for final sentiment classification.Experimental results demonstrate that the hybrid model outperforms traditional machine learning techniques and other deep learning models in terms of accuracy,precision,recall,and F1-score.By accurately classifying sentiment,identifying key themes,and predicting future trends,our model can provide valuable insights to businesses in the apparel industry.These insights can be used to improve product design,marketing strategies,and customer service,ultimately leading to increased customer satisfaction and business success.展开更多
Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured thro...Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured through big data.To address this gap,this study collects and analyzes public attention toward GC using web crawler technology.Based on the data from Sina Weibo,we applied RoBERTa,an advanced NLP model based on transformer architecture,to conduct fine-grained sentiment analysis of the public’s attention,attitudes and hot topics on GC,demonstrating the potential of deep learning methods in capturing dynamic and contextual emotional shifts across time and regions.Among the sample(N=188,509),53.91% expressed a positive attitude,with variation across different times and regions.Temporally,public interest in GC has shown an annual growth rate of 30.23%,gradually shifting fromfulfilling basic needs to prioritizing entertainment consumption.Spatially,GC is most prevalent in the southeast coastal regions of China,with Beijing ranking first across five evaluated domains.Individuals and government-affiliated accounts play a key role in public discussions on social networks,accounting for 45.89% and 30.01% of user reviews,respectively.A significant positive correlation exists between economic development and public attention to GC,as indicated by a Pearson correlation coefficient of 0.55.Companies,in particular,exhibit cautious behavior in the early stages of green product adoption,prioritizing profitability before making substantial investments.These findings provide valuable insights into the evolving public perception of GC,contributing to the development of more effective environmental policies in China.展开更多
Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a ...Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction.Conventional design methodologies such as object-oriented design methodology(OODM)have been proposed for web-based application development,which facilitates code reuse,quantification,and security at the design level.However,OODM did not provide the feature of explainability in web-based decision-making systems.X-OODM modifies the OODM with added explainable models to introduce the explainability feature for such systems.This research introduces an explainable model leveraging X-OODM for designing transparent applications for multidomain sentiment analysis.The proposed design is evaluated using the design quality metrics defined for the evaluation of the X-OODM explainable model under user context.The design quality metrics,transferability,simulatability,informativeness,and decomposability were introduced one after another over time to the evaluation of the X-OODM user context.Auxiliary metrics of accessibility and algorithmic transparency were added to increase the degree of explainability for the design.The study results reveal that introducing such explainability parameters with X-OODM appropriately increases system transparency,trustworthiness,and user understanding.The experimental results validate the enhancement of decision-making for multi-domain sentiment analysis with integration at the design level of explainability.Future work can be built in this direction by extending this work to apply the proposed X-OODM framework over different datasets and sentiment analysis applications to further scrutinize its effectiveness in real-world scenarios.展开更多
In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance o...In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance of two advanced models,the Large Language Model(LLM)LLaMA model and NLP BERT model,in the context of airline review sentiment analysis.Through fine-tuning,domain adaptation,and the application of few-shot learning,the study addresses the subtleties of sentiment expressions in airline-related text data.Employing predictive modeling and comparative analysis,the research evaluates the effectiveness of Large Language Model Meta AI(LLaMA)and Bidirectional Encoder Representations from Transformers(BERT)in capturing sentiment intricacies.Fine-tuning,including domain adaptation,enhances the models'performance in sentiment classification tasks.Additionally,the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis.By conducting experiments on a diverse airline review dataset,the research quantifies the impact of fine-tuning,domain adaptation,and few-shot learning on model performance,providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content(UGC).This research contributes to refining sentiment analysis models,ultimately fostering improved customer satisfaction in the airline industry.展开更多
Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,...Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,most existing works employ character faces in conjunction with context,yet lack the capacity to analyze the emotions of characters in unconstrained environments,such as when their faces are obscured or blurred.Accordingly,this article presents the Adaptive Multi-Channel Sentiment Analysis Network(AMSA),a contextual image sentiment analysis framework,which consists of three channels:body,face,and context.AMSA employs Multi-task Cascaded Convolutional Networks(MTCNN)to detect faces within body frames;if detected,facial features are extracted and fused with body and context information for emotion recognition.If not,the model leverages body and context features alone.Meanwhile,to address class imbalance in the EMOTIC dataset,Focal Loss is introduced to improve classification performance,especially for minority emotion categories.Experimental results have shown that certain sentiment categories with lower representation in the dataset demonstrate leading classification accuracy,the AMSA yields a 2.53%increase compared with state-of-the-art methods.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is among the most aggressive primary liver cancers,leading to significant global mortality.While early diagnosis improves prognosis,treatment decisions,particularly between surg...BACKGROUND Hepatocellular carcinoma(HCC)is among the most aggressive primary liver cancers,leading to significant global mortality.While early diagnosis improves prognosis,treatment decisions,particularly between surgical resection and radiofrequency ablation(RFA),remain controversial.AIM To clarify this issue using sentiment analysis of medical literature alongside a meta-analysis of overall survival(OS).METHODS We included studies comparing liver resection and RFA,excluding case reports,editorials,and studies without relevant outcomes.A systematic search in PubMed and Web of Science identified 197 studies.Abstracts underwent sentiment analysis using Python’s Natural Language Toolkit library,categorizing them as favoring resection,ablation,or neutral.We also performed a meta-analysis using a random-effects model on 11 studies reporting hazard ratios(HRs)for OS.RESULTS Sentiment analysis revealed that 75.1%of abstracts were neutral,14.2%favored resection,and 10.7%favored RFA.The meta-analysis showed a significant survival advantage for liver resection,with a pooled HR of 0.5924(95%confidence interval:0.540-0.649).Heterogeneity was moderate(I²=39.98%).Despite the meta-analysis demonstrating clear survival benefits of liver resection,most abstracts maintained a neutral stance.This discrepancy highlights potential biases or hesitancy in drawing definitive conclusions.CONCLUSION The study emphasizes the need for clinicians to prioritize robust statistical evidence over narrative impressions.Liver resection remains the preferred treatment for HCC in eligible patients.展开更多
Dialectal Arabic text classifcation(DA-TC)provides a mechanism for performing sentiment analysis on recent Arabic social media leading to many challenges owing to the natural morphology of the Arabic language and its ...Dialectal Arabic text classifcation(DA-TC)provides a mechanism for performing sentiment analysis on recent Arabic social media leading to many challenges owing to the natural morphology of the Arabic language and its wide range of dialect variations.Te availability of annotated datasets is limited,and preprocessing of the noisy content is even more challenging,sometimes resulting in the removal of important cues of sentiment from the input.To overcome such problems,this study investigates the applicability of using transfer learning based on pre-trained transformer models to classify sentiment in Arabic texts with high accuracy.Specifcally,it uses the CAMeLBERT model fnetuned for the Multi-Domain Arabic Resources for Sentiment Analysis(MARSA)dataset containing more than 56,000 manually annotated tweets annotated across political,social,sports,and technology domains.Te proposed method avoids extensive use of preprocessing and shows that raw data provides better results because they tend to retain more linguistic features.Te fne-tuned CAMeLBERT model produces state-of-the-art accuracy of 92%,precision of 91.7%,recall of 92.3%,and F1-score of 91.5%,outperforming standard machine learning models and ensemble-based/deep learning techniques.Our performance comparisons against other pre-trained models,namely AraBERTv02-twitter and MARBERT,show that transformer-based architectures are consistently the best suited when dealing with noisy Arabic texts.Tis work leads to a strong remedy for the problems in Arabic sentiment analysis and provides recommendations on easy tuning of the pre-trained models to adapt to challenging linguistic features and domain-specifc tasks.展开更多
Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk o...Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.展开更多
基金supported by the Science and Technology Project of Henan Province(No.222102210081).
文摘Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
基金funded by the University of Jeddah,Saudi Arabia,under Grant No.(UJ-12-18-DR).
文摘Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some sentiment.Therefore,sentiment analysis is currently a hot topic of research.In particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of polarity.It is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or neutral.Currently,the available lexicon-based method accuracy is affected by limited coverage.Several of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different topics.This paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify ABSA-PER.Experimental outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.
文摘In the era of digital communication,the exponential growth of user-generated content across social media and online platforms has intensified the demand for effective emotion analysis tools.Traditional text-based sentiment analysis methods,however,often fall short in accurately capturing the nuances of human emotions due to their reliance on a single modality.Motivated by the need for more comprehensive and context-aware emotion recognition,this study systematically reviews the literature on both unimodal and multimodal aspect-level sentiment analysis.By comparing different approaches within the multimodal domain,we identify existing challenges and emerging trends in this research area.Our findings highlight the potential of integrating multiple modalities—such as text,images,and audio—to enhance the precision of sentiment detection and suggest future directions for advancing multimodal sentiment analysis.
基金supported by the National Natural Science Foundation of China(Grant No.62376178)the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘1 Introduction This paper focuses on sentiment quadruple extraction in Chinese aspect-based sentiment analysis(ABSA),which involves identifying aspect terms,categories,opinion terms,and sentiment polarities.While existing studies focus solely on text-based analysis,we propose multimodal Chinese ABSA,integrating text and acoustic content to leverage their complementary roles.Motivated by the unique linguistic and emotional cues in speech,we construct the first large-scale Chinese ABSA dataset(12,918 sentences,157 hours of speech)with full annotations for all four sentiment elements.In Chinese,prosodic pauses and pitch variations inherently encode linguistic boundaries,enabling accurate word segmentation.Additionally,acoustic signals resolve ambiguities in multisyllabic homographs(e.g.,distinguishing meanings via pronunciation)and disambiguate textual uncertainties through phonetic nuances.
基金supported in part by the National Key Research and Development Program of China under Grant 2022YFB3102904in part by the National Natural Science Foundation of China under Grant No.U23A20305 and No.62472440.
文摘With the growing demand formore comprehensive and nuanced sentiment understanding,Multimodal Sentiment Analysis(MSA)has gained significant traction in recent years and continues to attract widespread attention in the academic community.Despite notable advances,existing approaches still face critical challenges in both information modeling and modality fusion.On one hand,many current methods rely heavily on encoders to extract global features from each modality,which limits their ability to capture latent fine-grained emotional cues within modalities.On the other hand,prevailing fusion strategies often lack mechanisms to model semantic discrepancies across modalities and to adaptively regulate modality interactions.To address these limitations,we propose a novel framework for MSA,termed Multi-Granularity Guided Fusion(MGGF).The proposed framework consists of three core components:(i)Multi-Granularity Feature Extraction Module,which simultaneously captures both global and local emotional features within each modality,and integrates them to construct richer intra-modal representations;(ii)Cross-ModalGuidance Learning Module(CMGL),which introduces a cross-modal scoring mechanism to quantify the divergence and complementarity betweenmodalities.These scores are then used as guiding signals to enable the fusion strategy to adaptively respond to scenarios of modality agreement or conflict;(iii)Cross-Modal Fusion Module(CMF),which learns the semantic dependencies among modalities and facilitates deep-level emotional feature interaction,thereby enhancing sentiment prediction with complementary information.We evaluate MGGF on two benchmark datasets:MVSA-Single and MVSA-Multiple.Experimental results demonstrate that MGGF outperforms the current state-of-the-art model CLMLF on MVSA-Single by achieving a 2.32% improvement in F1 score.On MVSA-Multiple,it surpasses MGNNS with a 0.26% increase in accuracy.These results substantiate the effectiveness ofMGGFin addressing two major limitations of existing methods—insufficient intra-modal fine-grained sentiment modeling and inadequate cross-modal semantic fusion.
基金This work was supported by the National Natural Science Foundation of China(No.61976247).
文摘The aspect-based sentiment analysis(ABSA)consists of two subtasksaspect term extraction and aspect sentiment prediction.Most methods conduct the ABSA task by handling the subtasks in a pipeline manner,whereby problems in performance and real application emerge.In this study,we propose an end-to-end ABSA model,namely,SSi-LSi,which fuses the syntactic structure information and the lexical semantic information,to address the limitation that existing end-to-end methods do not fully exploit the text information.Through two network branches,the model extracts syntactic structure information and lexical semantic information,which integrates the part of speech,sememes,and context,respectively.Then,on the basis of an attention mechanism,the model further realizes the fusion of the syntactic structure information and the lexical semantic information to obtain higher quality ABSA results,in which way the text information is fully used.Subsequent experiments demonstrate that the SSi-LSi model has certain advantages in using different text information.
基金supported by the National Natural Science Foundation of China(No.61976247)
文摘The aspect-based sentiment analysis(ABSA) consists of two subtasks—aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network(MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. Meanwhile, the representation obtained from the branch network of the main task is regarded as the global view, whereas the representations of the two subtasks are considered two local views with different emphases. Through multitask learning, the main task can be facilitated by additional accurate aspect boundary information and sentiment polarity information. By enhancing the correlations between the views under the idea of multiview learning, the representation of the global view can be optimized to improve the overall performance of the model. The experimental results on three benchmark datasets show that the proposed method exceeds the existing pipeline methods and end-to-end methods, proving the superiority of our MTMVN architecture.
基金supported by the National Natural Science Foundation of China under Grant 61976158 and Grant 61673301.
文摘The Aspect-Based Sentiment Analysis(ABSA)task is designed to judge the sentiment polarity of a particular aspect in a review.Recent studies have proved that GCN can capture syntactic and semantic features from dependency graphs generated by dependency trees and semantic graphs generated by Multi-headed self-attention(MHSA).However,these approaches do not highlight the sentiment information associated with aspect in the syntactic and semantic graphs.We propose the Aspect-Guided Multi-Graph Convolutional Networks(AGGCN)for Aspect-Based Sentiment Classification.Specifically,we reconstruct two kinds of graphs,changing the weight of the dependency graph by distance from aspect and improving the semantic graph by Aspect-guided MHSA.For interactive learning of syntax and semantics,we dynamically fuse syntactic and semantic diagrams to generate syntactic-semantic graphs to learn emotional features jointly.In addition,Multi-dropout is added to solve the overftting of AGGCN in training.The experimental results on extensive datasets show that our model AGGCN achieves particularly advanced results and validates the effectiveness of the model.
基金This work is supported by Opening fund of Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content(No.zd2022-10/02).
文摘Peer reviews of academic articles contain reviewers’ overall impressions and specific comments on the contributed articles,which have a lot of sentimental information.By exploring the fine-grained sentiments in peer reviews,we can discover critical aspects of interest to the reviewers.The results can also assist editors and chairmen in making final decisions.However,current research on the aspects of peer reviews is coarse-grained,and mostly focuses on the overall evaluation of the review objects.Therefore,this paper constructs a multi-level fine-grained aspect set of peer reviews for further study.First,this paper uses the multi-level aspect extraction method to extract the aspects from peer reviews of ICLR conference papers.Comparative experiments confirm the validity of the method.Secondly,various Deep Learning models are used to classify aspects’ sentiments automatically,with LCFS-BERT performing best.By calculating the correlation between sentimental scores of the review aspects and the acceptance result of papers,we can find the important aspects affecting acceptance.Finally,this paper predicts acceptance results of papers(accepted/rejected) according to the peer reviews.The optimal acceptance prediction model is XGboost,achieving a Macro_F1 score of 87.43%.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under Grant No.DGSSR-2024-02-01011.
文摘Wireless technologies and the Internet of Things(IoT)are being extensively utilized for advanced development in traditional communication systems.This evolution lowers the cost of the extensive use of sensors,changing the way devices interact and communicate in dynamic and uncertain situations.Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system.Therefore,it leads to the design of effective and trusted routing to support sustainable smart cities.This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model,which combines big data analytics and analysis rules to evaluate user feedback.The sentiment analysis is utilized to assess the perception of network performance,allowing the classification of device behavior as positive,neutral,or negative.By integrating sentiment-driven insights,the IoT network adjusts the system configurations to enhance the performance using network behaviour in terms of latency,reliability,fault tolerance,and sentiment score.Accordingly to the analysis,the proposed model categorizes the behavior of devices as positive,neutral,or negative,facilitating real-time monitoring for crucial applications.Experimental results revealed a significant improvement in the proposed model for threat prevention and network efficiency,demonstrating its resilience for real-time IoT applications.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
文摘The efficient market hypothesis in traditional financial theory struggles to explain the short-term irrational fluctuations in the A-share market,where investor sentiment fluctuations often serve as the core driver of abnormal stock price movements.Traditional sentiment measurement methods suffer from limitations such as lag,high misjudgment rates,and the inability to distinguish confounding factors.To more accurately explore the dynamic correlation between investor sentiment and stock price fluctuations,this paper proposes a sentiment analysis framework based on large language models(LLMs).By constructing continuous sentiment scoring factors and integrating them with a long short-term memory(LSTM)deep learning model,we analyze the correlation between investor sentiment and stock price fluctuations.Empirical results indicate that sentiment factors based on large language models can generate an annualized excess return of 9.3%in the CSI 500 index domain.The LSTM stock price prediction model incorporating sentiment features achieves a mean absolute percentage error(MAPE)as low as 2.72%,significantly outperforming traditional models.Through this analysis,we aim to provide quantitative references for optimizing investment decisions and preventing market risks.
文摘Theburgeoning e-commerce industry hasmade online customer reviews a crucial source of feedback for businesses.Sentiment analysis,a technique used to extract subjective information from text,has become essential for understanding consumer sentiment and preferences.However,traditional sentiment analysis methods often struggle with the nuances and context of natural language.To address these issues,this study proposes a comparison of deep learningmodels that figure out the optimalmethod to accurately analyze consumer reviews onwomen’s clothing.CNNs excel at capturing local features and semantic information,while LSTMs are adept at handling long-range dependencies and contextual understanding.By integrating these two deep learning techniques,our model aims to achieve better performance in sentiment classification.The models were trained and evaluated on a dataset of women’s clothing reviews sourced from Kaggle.The dataset was pre-processed to clean and tokenize the text data,and word embeddings were used to represent words as numerical vectors.The CNN component of the model extracts local features from the text,while the LSTM component captures long-range dependencies and contextual information.The outputs of the CNN and LSTM layers are then concatenated and fed into a fully connected layer for final sentiment classification.Experimental results demonstrate that the hybrid model outperforms traditional machine learning techniques and other deep learning models in terms of accuracy,precision,recall,and F1-score.By accurately classifying sentiment,identifying key themes,and predicting future trends,our model can provide valuable insights to businesses in the apparel industry.These insights can be used to improve product design,marketing strategies,and customer service,ultimately leading to increased customer satisfaction and business success.
基金supported by the National Nature Foundation of China under Grants(No.72104108)the College Students’Innovation and Entrepreneurship Training Program(No.202410298155Y).
文摘Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured through big data.To address this gap,this study collects and analyzes public attention toward GC using web crawler technology.Based on the data from Sina Weibo,we applied RoBERTa,an advanced NLP model based on transformer architecture,to conduct fine-grained sentiment analysis of the public’s attention,attitudes and hot topics on GC,demonstrating the potential of deep learning methods in capturing dynamic and contextual emotional shifts across time and regions.Among the sample(N=188,509),53.91% expressed a positive attitude,with variation across different times and regions.Temporally,public interest in GC has shown an annual growth rate of 30.23%,gradually shifting fromfulfilling basic needs to prioritizing entertainment consumption.Spatially,GC is most prevalent in the southeast coastal regions of China,with Beijing ranking first across five evaluated domains.Individuals and government-affiliated accounts play a key role in public discussions on social networks,accounting for 45.89% and 30.01% of user reviews,respectively.A significant positive correlation exists between economic development and public attention to GC,as indicated by a Pearson correlation coefficient of 0.55.Companies,in particular,exhibit cautious behavior in the early stages of green product adoption,prioritizing profitability before making substantial investments.These findings provide valuable insights into the evolving public perception of GC,contributing to the development of more effective environmental policies in China.
基金support of the Deanship of Research and Graduate Studies at Ajman University under Projects 2024-IRG-ENiT-36 and 2024-IRG-ENIT-29.
文摘Incorporation of explainability features in the decision-making web-based systems is considered a primary concern to enhance accountability,transparency,and trust in the community.Multi-domain Sentiment Analysis is a significant web-based system where the explainability feature is essential for achieving user satisfaction.Conventional design methodologies such as object-oriented design methodology(OODM)have been proposed for web-based application development,which facilitates code reuse,quantification,and security at the design level.However,OODM did not provide the feature of explainability in web-based decision-making systems.X-OODM modifies the OODM with added explainable models to introduce the explainability feature for such systems.This research introduces an explainable model leveraging X-OODM for designing transparent applications for multidomain sentiment analysis.The proposed design is evaluated using the design quality metrics defined for the evaluation of the X-OODM explainable model under user context.The design quality metrics,transferability,simulatability,informativeness,and decomposability were introduced one after another over time to the evaluation of the X-OODM user context.Auxiliary metrics of accessibility and algorithmic transparency were added to increase the degree of explainability for the design.The study results reveal that introducing such explainability parameters with X-OODM appropriately increases system transparency,trustworthiness,and user understanding.The experimental results validate the enhancement of decision-making for multi-domain sentiment analysis with integration at the design level of explainability.Future work can be built in this direction by extending this work to apply the proposed X-OODM framework over different datasets and sentiment analysis applications to further scrutinize its effectiveness in real-world scenarios.
文摘In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance of two advanced models,the Large Language Model(LLM)LLaMA model and NLP BERT model,in the context of airline review sentiment analysis.Through fine-tuning,domain adaptation,and the application of few-shot learning,the study addresses the subtleties of sentiment expressions in airline-related text data.Employing predictive modeling and comparative analysis,the research evaluates the effectiveness of Large Language Model Meta AI(LLaMA)and Bidirectional Encoder Representations from Transformers(BERT)in capturing sentiment intricacies.Fine-tuning,including domain adaptation,enhances the models'performance in sentiment classification tasks.Additionally,the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis.By conducting experiments on a diverse airline review dataset,the research quantifies the impact of fine-tuning,domain adaptation,and few-shot learning on model performance,providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content(UGC).This research contributes to refining sentiment analysis models,ultimately fostering improved customer satisfaction in the airline industry.
文摘Given the importance of sentiment analysis in diverse environments,various methods are used for image sentiment analysis,including contextual sentiment analysis that utilizes character and scene relationships.However,most existing works employ character faces in conjunction with context,yet lack the capacity to analyze the emotions of characters in unconstrained environments,such as when their faces are obscured or blurred.Accordingly,this article presents the Adaptive Multi-Channel Sentiment Analysis Network(AMSA),a contextual image sentiment analysis framework,which consists of three channels:body,face,and context.AMSA employs Multi-task Cascaded Convolutional Networks(MTCNN)to detect faces within body frames;if detected,facial features are extracted and fused with body and context information for emotion recognition.If not,the model leverages body and context features alone.Meanwhile,to address class imbalance in the EMOTIC dataset,Focal Loss is introduced to improve classification performance,especially for minority emotion categories.Experimental results have shown that certain sentiment categories with lower representation in the dataset demonstrate leading classification accuracy,the AMSA yields a 2.53%increase compared with state-of-the-art methods.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is among the most aggressive primary liver cancers,leading to significant global mortality.While early diagnosis improves prognosis,treatment decisions,particularly between surgical resection and radiofrequency ablation(RFA),remain controversial.AIM To clarify this issue using sentiment analysis of medical literature alongside a meta-analysis of overall survival(OS).METHODS We included studies comparing liver resection and RFA,excluding case reports,editorials,and studies without relevant outcomes.A systematic search in PubMed and Web of Science identified 197 studies.Abstracts underwent sentiment analysis using Python’s Natural Language Toolkit library,categorizing them as favoring resection,ablation,or neutral.We also performed a meta-analysis using a random-effects model on 11 studies reporting hazard ratios(HRs)for OS.RESULTS Sentiment analysis revealed that 75.1%of abstracts were neutral,14.2%favored resection,and 10.7%favored RFA.The meta-analysis showed a significant survival advantage for liver resection,with a pooled HR of 0.5924(95%confidence interval:0.540-0.649).Heterogeneity was moderate(I²=39.98%).Despite the meta-analysis demonstrating clear survival benefits of liver resection,most abstracts maintained a neutral stance.This discrepancy highlights potential biases or hesitancy in drawing definitive conclusions.CONCLUSION The study emphasizes the need for clinicians to prioritize robust statistical evidence over narrative impressions.Liver resection remains the preferred treatment for HCC in eligible patients.
基金funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘Dialectal Arabic text classifcation(DA-TC)provides a mechanism for performing sentiment analysis on recent Arabic social media leading to many challenges owing to the natural morphology of the Arabic language and its wide range of dialect variations.Te availability of annotated datasets is limited,and preprocessing of the noisy content is even more challenging,sometimes resulting in the removal of important cues of sentiment from the input.To overcome such problems,this study investigates the applicability of using transfer learning based on pre-trained transformer models to classify sentiment in Arabic texts with high accuracy.Specifcally,it uses the CAMeLBERT model fnetuned for the Multi-Domain Arabic Resources for Sentiment Analysis(MARSA)dataset containing more than 56,000 manually annotated tweets annotated across political,social,sports,and technology domains.Te proposed method avoids extensive use of preprocessing and shows that raw data provides better results because they tend to retain more linguistic features.Te fne-tuned CAMeLBERT model produces state-of-the-art accuracy of 92%,precision of 91.7%,recall of 92.3%,and F1-score of 91.5%,outperforming standard machine learning models and ensemble-based/deep learning techniques.Our performance comparisons against other pre-trained models,namely AraBERTv02-twitter and MARBERT,show that transformer-based architectures are consistently the best suited when dealing with noisy Arabic texts.Tis work leads to a strong remedy for the problems in Arabic sentiment analysis and provides recommendations on easy tuning of the pre-trained models to adapt to challenging linguistic features and domain-specifc tasks.
文摘Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
